The rsemmed User’s Guide
Introduction
The rsemmed package provides a way for users to explore connections between the biological concepts present in the Semantic MEDLINE database (Kilicoglu et al. 2011) in a programmatic way.
Overview of Semantic MEDLINE
The Semantic MEDLINE database (SemMedDB) is a collection of annotations of sentences from the abstracts of articles indexed in PubMed. These annotations take the form of subject-predicate-object triples of information. These triples are also called predications.
An example predication is “Interleukin-12 INTERACTS_WITH IFNA1”. Here, the subject is “Interleukin-12”, the object is “IFNA1” (interferon alpha-1), and the predicate linking the subject and object is “INTERACTS_WITH”. The Semantic MEDLINE database consists of tens of millions of these predications.
Semantic MEDLINE also provides information on the broad categories into which biological concepts (predication subjects and objects) fall. This information is called the semantic type of a concept. The databases assigns 4-letter codes to semantic types. For example, “gngm” represents “Gene or Genome”. Every concept in the database has one or more semantic types (abbreviated as “semtypes”).
Note: The information in Semantic MEDLINE is primarily computationally-derived. Thus, some information will seem nonsensical. For example, the reported semantic types of concepts might not quite match. The Semantic MEDLINE resource and this package are meant to facilitate an initial window of exploration into the literature. The hope is that this package helps guide more streamlined manual investigations of the literature.
Graph representation of SemMedDB
The predications in SemMedDB can be represented in graph form. Nodes represent concepts, and directed edges represent predicates (concept linkers). In particular, the Semantic MEDLINE graph is a directed multigraph because multiple predicates are often present between pairs of nodes (e.g., “A ASSOCIATED_WITH B” and “A INTERACTS_WITH B”). rsemmed relies on the igraph package for efficient graph operations.
Full data availability
The full data underlying the complete Semantic MEDLINE database is available from from this National Library of Medicine site as SQL dump files. In particular, the PREDICATION table is the primary file that is needed to construct the database. More information about the Semantic MEDLINE database is available here.
See the inst/script folder for scripts to perform the
following processing of these raw files:
- Conversion of the original SQL dump files to a CSV file
- Generation of the graph representation from the CSV file
The next section describes details about the processing that occurs in these scripts to generate the graph representation.
In this vignette, we will explore a much smaller subset of the full graph that suffices to show the full functionality of rsemmed.
Note about processed data
The graph representation of SemMedDB contains a processed and summarized form of the raw database. The toy example below illustrates the summarization performed.
| Subject | Subject semtype | Predicate | Object | Object semtype |
|---|---|---|---|---|
| A | aapp | INHIBITS | B | gngm |
| A | gngm | INHIBITS | B | aapp |
The two rows show two predications that are treated as different predications because the semantic types (“semtypes”) of the subject and object vary. In the processed data, such instances have been collapsed as shown below.
| Subject | Subject semtype | Predicate | Object | Object semtype | # instances |
|---|---|---|---|---|---|
| A | aapp,gngm | INHIBITS | B | aapp,gngm | 2 |
The different semantic types for a particular concept are collapsed
into a single comma-separated string that is available via
igraph::vertex_attr(g, "semtype").
The “# instances” column indicates that the “A INHIBITS B”
predication was observed twice in the database. This piece of
information is available as an edge attribute via
igraph::edge_attr(g, "num_instances"). Similarly, predicate
information is also an edge attribute accessible via
igraph::edge_attr(g, "predicate").
A note of caution: Be careful when working with edge attributes in the Semantic MEDLINE graph manually. These operations can be very slow because there are over 18 million edges. Working with node/vertex attributes is much faster, but there are still a very large number of nodes (roughly 290,000).
The rest of this vignette will showcase how to use rsemmed functions to explore this graph.
Installation
To install rsemmed, start R and enter the following:
Example workflow
Loading packages and data
Load the rsemmed
package and the g_small object which contains a smaller
version of the Semantic MEDLINE database.
## Loading required package: igraph##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionThis loads an object of class igraph named
g_small into the workspace. The SemMedDB graph object is a
necessary input for most of rsemmed’s
functions.
(The full processed graph representation linked above contains an
object of class igraph named g.)
Finding nodes
The starting point for an rsemmed exploration is to find nodes related to the initial ideas of interest. For example, we may wish to find connections between the ideas “sickle cell trait” and “malaria”.
The rsemmed::find_nodes() function allows you to search
for nodes by name. We supply the graph and a regular expression to use
in searching through the name attribute of the nodes.
Finding the most relevant nodes will generally involve iteration.
To find nodes related to the sickle cell trait, we can start by searching for nodes containing the word “sickle”. (Note: searches ignore capitalization.)
## This graph was created by an old(er) igraph version.
## ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version.
## For now we convert it on the fly...## + 5/1038 vertices, named, from f0c97f6:
## [1] Sickle Cell Anemia Sickle Hemoglobin Sickle Cell Trait
## [4] Sickle cell retinopathy sickle traitWe may decide that only sickle cell anemia and the sickle trait are important. Conventional R subsetting allows us to keep the 3 related nodes:
## + 3/1038 vertices, named, from f0c97f6:
## [1] Sickle Cell Anemia Sickle Cell Trait sickle traitWe can also search for nodes related to “malaria”:
## + 32/1038 vertices, named, from f0c97f6:
## [1] Malaria
## [2] Malaria, Falciparum
## [3] Malaria, Cerebral
## [4] Malaria Vaccines
## [5] Antimalarials
## [6] Malaria, Vivax
## [7] Simian malaria
## [8] Malaria, Avian
## [9] Malarial parasites
## [10] Mixed malaria
## + ... omitted several verticesThere are 32 results, not all of which are printed, so we can display
all results by accessing the name attribute of the returned
nodes:
## [1] "Malaria"
## [2] "Malaria, Falciparum"
## [3] "Malaria, Cerebral"
## [4] "Malaria Vaccines"
## [5] "Antimalarials"
## [6] "Malaria, Vivax"
## [7] "Simian malaria"
## [8] "Malaria, Avian"
## [9] "Malarial parasites"
## [10] "Mixed malaria"
## [11] "Plasmodium malariae infection"
## [12] "Malaria antigen"
## [13] "Prescription of prophylactic anti-malarial"
## [14] "Induced malaria"
## [15] "Malaria serology"
## [16] "Algid malaria"
## [17] "Aminoquinoline antimalarial"
## [18] "Plasmodium malariae"
## [19] "Malaria antibody"
## [20] "Malaria screening"
## [21] "Congenital malaria"
## [22] "Ovale malaria"
## [23] "Quartan malaria"
## [24] "Malaria Antibodies Test"
## [25] "Biguanide antimalarial"
## [26] "MALARIA RELAPSE"
## [27] "KIT, TEST, MALARIA"
## [28] "Malarial hepatitis"
## [29] "Malarial pigmentation"
## [30] "Malaria antigen test"
## [31] "Malarial nephrosis"
## [32] "Malaria smear"Perhaps we only want to keep the nodes that relate to disease. We
could use direct subsetting, but another option is to use
find_nodes() again with nodes_malaria as the
input. Using the match argument set to FALSE
allows us to prune unwanted matches from our results.
Below we iteratively prune matches to only keep disease-related results. Though this is not as condense as direct subsetting, it is more transparent about what was removed.
nodes_malaria <- nodes_malaria %>%
find_nodes(pattern = "anti", match = FALSE) %>%
find_nodes(pattern = "test", match = FALSE) %>%
find_nodes(pattern = "screening", match = FALSE) %>%
find_nodes(pattern = "pigment", match = FALSE) %>%
find_nodes(pattern = "smear", match = FALSE) %>%
find_nodes(pattern = "parasite", match = FALSE) %>%
find_nodes(pattern = "serology", match = FALSE) %>%
find_nodes(pattern = "vaccine", match = FALSE)
nodes_malaria## + 17/1038 vertices, named, from f0c97f6:
## [1] Malaria Malaria, Falciparum
## [3] Malaria, Cerebral Malaria, Vivax
## [5] Simian malaria Malaria, Avian
## [7] Mixed malaria Plasmodium malariae infection
## [9] Induced malaria Algid malaria
## [11] Plasmodium malariae Congenital malaria
## [13] Ovale malaria Quartan malaria
## [15] MALARIA RELAPSE Malarial hepatitis
## [17] Malarial nephrosisThe find_nodes() function can also be used with the
semtypes argument which allows you to specify a character
vector of semantic types to search for. If both pattern and
semtypes are provided, they are combined with an
OR operation. If you would like them to be combined with an
AND operation, nest the calls in sequence.
## + 317/1038 vertices, named, from f0c97f6:
## [1] Obstruction
## [2] Depressed mood
## [3] Carcinoma
## [4] HIV-1
## [5] Infection
## [6] leukemia
## [7] Neoplasm
## [8] Renal tubular disorder
## [9] Toxic effect
## [10] Vesicle
## + ... omitted several vertices## malaria AND disease (dsyn)
find_nodes(g_small, pattern = "malaria") %>%
find_nodes(semtypes = "dsyn")## + 16/1038 vertices, named, from f0c97f6:
## [1] Malaria Malaria, Falciparum
## [3] Malaria, Cerebral Malaria, Vivax
## [5] Simian malaria Malaria, Avian
## [7] Mixed malaria Plasmodium malariae infection
## [9] Induced malaria Algid malaria
## [11] Congenital malaria Ovale malaria
## [13] Quartan malaria MALARIA RELAPSE
## [15] Malarial hepatitis Malarial nephrosisFinally, you can also select nodes by exact name with the
names argument. (Capitalization is ignored.)
## + 1/1038 vertex, named, from f0c97f6:
## [1] sickle trait## + 1/1038 vertex, named, from f0c97f6:
## [1] sickle traitGrowing understanding by connecting nodes
Now that we have nodes related to the ideas of interest, we can develop further understanding by asking the following questions:
- How are these node sets connected to each other? (Aim 1)
- What ideas (nodes) are connected to these nodes? (Aim 2)
Aim 1: Connecting different node sets
To further Aim 1, we can use the rsemmed::find_paths()
function. This function takes two sets of nodes from and
to (corresponding to the two different ideas of interest)
and returns all shortest paths between nodes in from
(“source” nodes) and nodes in to (“target” nodes). That is,
for every possible combination of a single node in from and
a single node in to, all shortest undirected paths
between those nodes are found.
Information from find_paths()
The result of find_paths() is a list with one element
for each of the nodes in from. Each element is itself a
list of paths between from and to. In igraph, paths
are represented as vertex sequences (class igraph.vs).
Recall that nodes_sickle contains the nodes below:
## + 3/1038 vertices, named, from f0c97f6:
## [1] Sickle Cell Anemia Sickle Cell Trait sickle traitThus, paths is structured as follows:
paths[[1]]is a list of paths originating from Sickle Cell Anemia.paths[[2]]is a list of paths originating from Sickle Cell Trait.paths[[3]]is a list of paths originating from sickle trait.
With lengths() we can show the number of shortest paths
starting at each of the three source (“from”) nodes:
## [1] 956 268 1601Displaying paths
There are two ways to display the information contained in these
paths: rsemmed::text_path() and
rsemmed::plot_path().
text_path()displays a text version of a pathplot_path()displays a graphical version of the path
For example, to show the 100th of the shortest paths originating from
the first of the sickle trait nodes (paths[[1]][[100]]), we
can use text_path() and plot_path() as
below:
## Sickle Cell Anemia --- pulmonary complications :
## # A tibble: 4 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Sickle Cell Anemia CAUSES pulmonary co… patf
## 2 patf pulmonary complications CAUSES Sickle Cell … dsyn
## 3 patf pulmonary complications COEXISTS_WITH Sickle Cell … dsyn
## 4 patf pulmonary complications MANIFESTATION_OF Sickle Cell … dsyn
##
## pulmonary complications --- Malaria, Falciparum :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 patf pulmonary complications COEXISTS_WITH Malaria, Falcip… dsyn## [[1]]
## # A tibble: 4 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Sickle Cell Anemia CAUSES pulmonary co… patf
## 2 patf pulmonary complications CAUSES Sickle Cell … dsyn
## 3 patf pulmonary complications COEXISTS_WITH Sickle Cell … dsyn
## 4 patf pulmonary complications MANIFESTATION_OF Sickle Cell … dsyn
##
## [[2]]
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 patf pulmonary complications COEXISTS_WITH Malaria, Falcip… dsyn
plot_path() plots the subgraph defined by the nodes on
the path.
text_path() sequentially shows detailed information
about semantic types and predicates for the pairs of nodes on the path.
It also invisibly returns a list of tibble’s containing the
displayed information, where each list element corresponds to a pair of
nodes on the path.
Refining paths with weights
Finding paths between node sets necessarily uses shortest path algorithms for computational tractability. However, when these algorithms are run without modification, the shortest paths tend to be less useful than desired.
For example, one of the shortest paths from “sickle trait” to “Malaria, Cerebral” goes through the node “Infant”:
This likely isn’t the type of path we were hoping for. Why does such
a path arise? For some insight, we can use the degree()
function within the igraph package
to look at the degree distribution for all nodes in the Semantic MEDLINE
graph. We also show the degree of the “Infant” node in red.
plot(density(degree(g_small), from = 0),
xlab = "Degree", main = "Degree distribution")
## The second node in the path is "Infant" --> this_path[2]
abline(v = degree(g_small, v = this_path[2]), col = "red", lwd = 2)
We can see why “Infant” would be on a shortest path connecting “sickle trait” and “Malaria, Cerebral”. “Infant” has a very large degree, and most of its connections are likely of the uninteresting form “PROCESS_OF” (a predicate indicating that the subject node is a biological process that occurs in the organism represented by the object node).
We can discourage such paths from consideration by modifying edge
weights. By default, all edges have a weight of 1 in the shortest path
search, but we can effectively block off certain edges by giving them a
high enough weight. For example, in
rsemmed::make_edge_weights(), this weight is chosen to
equal the number of nodes in the entire graph. (Note that if
all paths from the source node to the target node
contain a given undesired edge, the process of edge reweighting will not
prevent paths from containing that edge.)
The process of modifying edge weights starts by obtaining
characteristics for all of the edges in the Semantic MEDLINE graph. This
is achieved with the rsemmed::get_edge_features()
function:
## # A tibble: 6 × 5
## node_subj_name node_subj_semtypes node_obj_name node_obj_semtypes edge_pred
## <chr> <chr> <chr> <chr> <chr>
## 1 infant aggp,humn control groups grup,humn,patf interact…
## 2 infant aggp,humn antibodies aapp,bpoc,gngm,i… neg_loca…
## 3 infant aggp,humn antibodies aapp,bpoc,gngm,i… location…
## 4 infant aggp,humn glutathione aapp,bacs,gngm,p… location…
## 5 infant aggp,humn glycoproteins aapp,carb,chvs,g… location…
## 6 infant aggp,humn granulocyte col… aapp,gngm,imft neg_loca…For every edge in the graph, the following information is returned in
a tibble:
- The name and semantic type (
semtype) of the subject and object node - The predicate for the edge
You can directly use the information from
get_edge_features() to manually construct custom weights
for edges. This could include giving certain edges maximal weights as
described above or encouraging certain edges by giving them lower
weights.
The get_edge_features() function also has arguments
include_degree, include_node_ids, and
include_num_instances which can be set to TRUE
to include additional edge features in the output.
include_degree: Adds information on the degree of the subject and object nodes and the degree percentile in the entire graph. (100th percentile = highest degree)include_node_ids: Adds the integer IDs for the subject and object nodes. This IDs can be useful with igraph functions that compute various node/vertex metrics (e.g., centrality measures withigraph::closeness(),igraph::edge_betweenness()).include_num_instances: Adds information on the number of times a particular edge (predication) was seen in the Semantic MEDLINE database. This might be useful if you want to weight edges based on how commonly the relationship was reported.
Weighting option: make_edge_weights()
The rsemmed::make_edge_weights() function provides a way
to create weights that encourage and/or discourage certain features. It
allows you to specify the node names, node semantic types, and edge
predicates that you would like to include in and/or exclude from
paths.
- The first two arguments
gande_featsupply required graph metadata. - “Out” arguments:
node_semtypes_out,node_names_out,edge_preds_outare supplied as character vectors of node semantic types, names, and edge predicates that you wish to exclude from shortest path results. These three features are combined with an OR operation. An edge that meets any one of these criteria is given the highest weight possible to discourage paths from including this edge. - “In” arguments:
node_semtypes_in,node_names_in,edge_preds_inare analogous to the “out” arguments but indicate types of edges you wish to include within shortest path results. Like with the “out arguments”, these three features are combined with an OR operation. An edge that meets any one of these criteria is given a lower weight to encourage paths to include this edge.
As an example of the impact of reweighting, let’s examine the connections between “sickle trait” and “Malaria, Cerebral”. In order to clearly see the effects of edge reweighting, below we obtain the paths from “sickle trait” to “Malaria, Cerebral”:
paths_subset <- find_paths(
graph = g_small,
from = find_nodes(g_small, names = "sickle trait"),
to = find_nodes(g_small, names = "Malaria, Cerebral")
)
paths_subset <- paths_subset[[1]]
par(mfrow = c(1,2), mar = c(3,0,1,0))
for (i in seq_along(paths_subset)) {
cat("Path", i, ": ==============================================\n")
text_path(g_small, paths_subset[[i]])
cat("\n")
plot_path(g_small, paths_subset[[i]])
}## Path 1 : ==============================================
## sickle trait --- Prophylactic treatment :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 hlca,topp Prophylactic treatment TREATS sickle trait fndg
##
## Prophylactic treatment --- Malaria, Cerebral :
## # A tibble: 2 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 hlca,topp Prophylactic treatment PREVENTS Malaria, Cerebral dsyn
## 2 hlca,topp Prophylactic treatment TREATS Malaria, Cerebral dsyn## Path 2 : ==============================================
## sickle trait --- Malaria, Falciparum :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Malaria, Falciparum COEXISTS_WITH sickle trait fndg
##
## Malaria, Falciparum --- Malaria, Cerebral :
## # A tibble: 7 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Malaria, Cerebral CAUSES Malaria, Falciparum dsyn
## 2 dsyn Malaria, Cerebral COEXISTS_WITH Malaria, Falciparum dsyn
## 3 dsyn Malaria, Cerebral COMPLICATES Malaria, Falciparum dsyn
## 4 dsyn Malaria, Cerebral ISA Malaria, Falciparum dsyn
## 5 dsyn Malaria, Cerebral PRECEDES Malaria, Falciparum dsyn
## 6 dsyn Malaria, Falciparum CAUSES Malaria, Cerebral dsyn
## 7 dsyn Malaria, Falciparum NEG_OCCURS_IN Malaria, Cerebral dsyn
## Path 3 : ==============================================
## sickle trait --- Death, Sudden :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 fndg,patf Death, Sudden COEXISTS_WITH sickle trait fndg
##
## Death, Sudden --- Malaria, Cerebral :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Malaria, Cerebral CAUSES Death, Sudden fndg,patf## Path 4 : ==============================================
## sickle trait --- Retinal Diseases :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Retinal Diseases COEXISTS_WITH sickle trait fndg
##
## Retinal Diseases --- Malaria, Cerebral :
## # A tibble: 4 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Malaria, Cerebral COEXISTS_WITH Retinal Diseases dsyn
## 2 dsyn Retinal Diseases AFFECTS Malaria, Cerebral dsyn
## 3 dsyn Retinal Diseases COEXISTS_WITH Malaria, Cerebral dsyn
## 4 dsyn Retinal Diseases PREDISPOSES Malaria, Cerebral dsyn
## Path 5 : ==============================================
## sickle trait --- Child :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 fndg sickle trait PROCESS_OF Child aggp,humn,inpr,popg
##
## Child --- Malaria, Cerebral :
## # A tibble: 4 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Malaria, Cerebral AFFECTS Child aggp,humn,inpr,popg
## 2 dsyn Malaria, Cerebral NEG_PROCESS_OF Child aggp,humn,inpr,popg
## 3 dsyn Malaria, Cerebral OCCURS_IN Child aggp,humn,inpr,popg
## 4 dsyn Malaria, Cerebral PROCESS_OF Child aggp,humn,inpr,popg## Path 6 : ==============================================
## sickle trait --- Woman :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 fndg sickle trait PROCESS_OF Woman anim,humn,popg
##
## Woman --- Malaria, Cerebral :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Malaria, Cerebral PROCESS_OF Woman anim,humn,popg
## Path 7 : ==============================================
## sickle trait --- Infant :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 fndg sickle trait PROCESS_OF Infant aggp,humn
##
## Infant --- Malaria, Cerebral :
## # A tibble: 1 × 5
## from_semtype from via to to_semtype
## <chr> <chr> <chr> <chr> <chr>
## 1 dsyn Malaria, Cerebral PROCESS_OF Infant aggp,humn
The “Child”, “Woman”, and “Infant” connections do not provide particularly useful biological insight. We could discourage paths from containing these nodes by specifically targeting those node names in the reweighting:
However, in case there are other similar nodes (like “Teens”), we might want to discourage this group of nodes by specifying the semantic type corresponding to this group. We can see the semantic types of the nodes on the shortest paths as follows:
## [[1]]
## [1] "fndg" "hlca,topp" "dsyn"
##
## [[2]]
## [1] "fndg" "dsyn" "dsyn"
##
## [[3]]
## [1] "fndg" "fndg,patf" "dsyn"
##
## [[4]]
## [1] "fndg" "dsyn" "dsyn"
##
## [[5]]
## [1] "fndg" "aggp,humn,inpr,popg" "dsyn"
##
## [[6]]
## [1] "fndg" "anim,humn,popg" "dsyn"
##
## [[7]]
## [1] "fndg" "aggp,humn" "dsyn"We can see that the “humn” and “popg” semantic types correspond to
the class of nodes we would like to discourage. We supply them in the
node_semtypes_out argument and repeat the path search with
these weights:
w <- make_edge_weights(g_small, e_feat, node_semtypes_out = c("humn", "popg"))
paths_subset_reweight <- find_paths(
graph = g_small,
from = find_nodes(g_small, names = "sickle trait"),
to = find_nodes(g_small, names = "Malaria, Cerebral"),
weights = w
)
paths_subset_reweight## [[1]]
## [[1]][[1]]
## + 3/1038 vertices, named, from f0c97f6:
## [1] sickle trait Retinal Diseases Malaria, Cerebral
##
## [[1]][[2]]
## + 3/1038 vertices, named, from f0c97f6:
## [1] sickle trait Death, Sudden Malaria, Cerebral
##
## [[1]][[3]]
## + 3/1038 vertices, named, from f0c97f6:
## [1] sickle trait Malaria, Falciparum Malaria, Cerebral
##
## [[1]][[4]]
## + 3/1038 vertices, named, from f0c97f6:
## [1] sickle trait Prophylactic treatment Malaria, CerebralThe effect of that reweighting was likely not quite what we wanted.
The discouraging of “humn” and “popg” nodes only served to filter down
the 7 original paths to 4 shortest paths. Because the first 4 of the 7
original paths were not explicitly removed through the reweighting, they
remained the shortest paths from source to target. If we would like to
see different types of paths (longer paths), we should indicate that we
would like to remove all of the original paths’ middle nodes. We can use
the rsemmed::get_middle_nodes() function to obtain a
character vector of names of middle nodes in a path set.
## Obtain the middle nodes (2nd node on the path)
out_names <- get_middle_nodes(g_small, paths_subset)## Joining with `by = join_by(name)`
## Joining with `by = join_by(path_group, path_id)`## Readjust weights
w <- make_edge_weights(g_small, e_feat,
node_names_out = out_names, node_semtypes_out = c("humn", "popg")
)
## Find paths with new weights
paths_subset_reweight <- find_paths(
graph = g_small,
from = find_nodes(g_small, pattern = "sickle trait"),
to = find_nodes(g_small, pattern = "Malaria, Cerebral"),
weights = w
)
paths_subset_reweight <- paths_subset_reweight[[1]]
## How many paths?
length(paths_subset_reweight)## [1] 1550There is clearly a much greater diversity of paths resulting from this search.
par(mfrow = c(1,2), mar = c(2,1.5,1,1.5))
plot_path(g_small, paths_subset_reweight[[1]])
plot_path(g_small, paths_subset_reweight[[2]])
When dealing with paths from several source and target nodes, it can
be helpful to obtain the middle nodes on paths for specific
source-target pairs. By default get_midddle_nodes() returns
a single character vector of middle node names across all of
the paths supplied. By using collapse = FALSE, the names of
middle nodes can be returned for every source-target pair. When
collapse = FALSE, this function enumerates all
source-target pairs in tibble form. For every pair of
source and target nodes in the paths object supplied, the final column
(called middle_nodes) provides the names of the middle
nodes as a character vector. (middle_nodes is a
list-column.)
## Joining with `by = join_by(name)`
## Joining with `by = join_by(path_group, path_id)`## # A tibble: 51 × 3
## # Groups: from [3]
## from to middle_nodes
## <chr> <chr> <list>
## 1 Sickle Cell Anemia Algid malaria <chr [2]>
## 2 Sickle Cell Anemia Congenital malaria <chr [33]>
## 3 Sickle Cell Anemia Induced malaria <chr [19]>
## 4 Sickle Cell Anemia MALARIA RELAPSE <chr [4]>
## 5 Sickle Cell Anemia Malaria <chr [0]>
## 6 Sickle Cell Anemia Malaria, Avian <chr [21]>
## 7 Sickle Cell Anemia Malaria, Cerebral <chr [323]>
## 8 Sickle Cell Anemia Malaria, Falciparum <chr [422]>
## 9 Sickle Cell Anemia Malaria, Vivax <chr [0]>
## 10 Sickle Cell Anemia Malarial hepatitis <chr [5]>
## # ℹ 41 more rowsThe make_edge_weights function can also encourage
certain features. Below we simultaneously discourage the
"humn" and "popg" semantic types and encourage
the "gngm" and "aapp" semantic types.
w <- make_edge_weights(g_small, e_feat,
node_semtypes_out = c("humn", "popg"),
node_semtypes_in = c("gngm", "aapp")
)
paths_subset_reweight <- find_paths(
graph = g_small,
from = find_nodes(g_small, pattern = "sickle trait"),
to = find_nodes(g_small, pattern = "Malaria, Cerebral"),
weights = w
)
paths_subset_reweight <- paths_subset_reweight[[1]]
length(paths_subset_reweight)## [1] 19Summarizing information in paths
When there are many shortest paths, it can be useful to get a
high-level summary of the nodes and edges on those paths. The
rsemmed::summarize_semtypes() function tabulates the
semantic types of nodes on paths, and the
rsemmed::summarize_predicates() functions tabulates the
predicates of the edges.
summarize_semtypes() removes the first and last node
from the paths by default because that information is generally easily
accessible by using nodes_from$semtype and
nodes_to$semtype. Further, if the start and end nodes are
not removed, they would be duplicated in the tabulation a number of
times equal to the number of paths, which likely is not desirable.
summarize_semtypes() invisibly returns a
tibble where each row corresponds to a pair of source
(from) and target (to) nodes in the paths
object supplied, and the final semtypes column is a
list-column containing a table of semantic type
information. It automatically prints the semantic type tabulations for
each from-to pair, but if you would like to
turn off printing, use print = FALSE.
## Reweighted paths from "sickle trait" to "Malaria, Cerebral"
semtype_summary <- summarize_semtypes(g_small, paths_subset_reweight)## Joining with `by = join_by(name)`
## Joining with `by = join_by(path_group, path_id)`## sickle trait ----------> Malaria, Cerebral
## semtypes
## aapp gngm bacs phsu imft topp horm lbpr orch bodm bdsu bpoc diap elii enzy genf
## 38 36 26 8 6 6 4 4 3 2 1 1 1 1 1 1
## hlca inch mbrt nusq strd virs
## 1 1 1 1 1 1## # A tibble: 1 × 3
## # Groups: from [1]
## from to semtypes
## <chr> <chr> <list>
## 1 sickle trait Malaria, Cerebral <table [22]>## semtypes
## aapp gngm bacs phsu imft topp horm lbpr orch bodm bdsu bpoc diap elii enzy genf
## 38 36 26 8 6 6 4 4 3 2 1 1 1 1 1 1
## hlca inch mbrt nusq strd virs
## 1 1 1 1 1 1## Joining with `by = join_by(name)`
## Joining with `by = join_by(path_group, path_id)`## Sickle Cell Anemia ----------> Algid malaria
## semtypes
## dsyn orch patf
## 2 1 1
##
## Sickle Cell Anemia ----------> Congenital malaria
## semtypes
## dsyn humn popg anim mamm patf phsu aggp famg emst fndg orch aapp bacs bact bdsu
## 12 11 7 5 4 4 4 3 3 2 2 2 1 1 1 1
## celc cell diap elii gngm hlca imft inpr invt lbpr neop orgf orgm sosy tisu
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Induced malaria
## semtypes
## gngm aapp phsu imft bpoc topp dsyn humn bacs horm mamm popg aggp anim carb chvs
## 9 8 7 6 4 4 3 3 2 2 2 2 1 1 1 1
## hlca inch inpr lbpr nsba orch ortf prog
## 1 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> MALARIA RELAPSE
## semtypes
## hlca humn invt mamm popg resa topp
## 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Malaria
## integer(0)
##
## Sickle Cell Anemia ----------> Malaria, Avian
## semtypes
## dsyn phsu aapp gngm orch bpoc elii fndg genf lbpr topp bacs bdsu celc chvf enzy
## 9 5 3 3 3 2 2 2 2 2 2 1 1 1 1 1
## hlca imft inch mbrt medd neop nusq orga orgf patf sosy tisu
## 1 1 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Malaria, Cerebral
## semtypes
## dsyn aapp gngm phsu topp bacs patf fndg hlca imft orch humn sosy bpoc lbpr popg
## 91 73 69 68 51 48 37 27 27 27 25 24 19 18 13 9
## horm inpo anim diap enzy mobd orgf comd aggp carb eico mamm medd prog acab elii
## 8 8 7 7 7 7 7 6 5 5 5 5 5 5 4 4
## lipd ortf chvf famg genf hops inch mbrt neop npop orga orgm podg tisu virs vita
## 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## antb celc celf chem chvs inpr irda nnon rcpt resa resd strd bdsu biof blor bodm
## 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
## emod emst grup idcn invt lbtr menp nusq phsf
## 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Malaria, Falciparum
## semtypes
## dsyn aapp gngm phsu bacs humn topp hlca patf fndg imft popg orch lbpr sosy bpoc
## 113 90 85 71 56 56 54 43 40 32 30 30 26 22 15 13
## orgf diap elii horm aggp orga prog virs chvf enzy famg genf inpo anim inch mbrt
## 13 9 9 9 8 7 7 7 6 6 6 6 6 5 5 5
## neop ortf acab carb hops mobd orgm podg vita celc cell chem comd grup lipd medd
## 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3
## nnon bodm celf chvs eico mamm mnob phsf rcpt resa tisu antb bact bdsu blor clna
## 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
## dora emst inpr invt lbtr npop nsba nusq resd strd
## 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Malaria, Vivax
## integer(0)
##
## Sickle Cell Anemia ----------> Malarial hepatitis
## semtypes
## dsyn comd diap hlca humn inpo patf popg
## 3 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Malarial nephrosis
## semtypes
## aggp humn inpr popg
## 1 1 1 1
##
## Sickle Cell Anemia ----------> Mixed malaria
## semtypes
## dsyn humn phsu popg aggp bacs bird chvs hlca inch inpr invt orch orgf patf
## 4 2 2 2 1 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Ovale malaria
## semtypes
## humn popg dsyn aapp gngm aggp phsu topp anim bacs enzy horm imft inpr medd patf
## 8 6 4 3 3 2 2 2 1 1 1 1 1 1 1 1
## prog
## 1
##
## Sickle Cell Anemia ----------> Plasmodium malariae
## semtypes
## dsyn patf invt
## 9 3 1
##
## Sickle Cell Anemia ----------> Plasmodium malariae infection
## semtypes
## dsyn humn gngm popg aapp lbpr phsu hlca topp aggp orch patf bpoc bacs cell elii
## 16 12 8 8 7 7 7 6 6 5 5 5 3 2 2 2
## fndg imft mbrt sosy virs anim bdsu celc clna comd diap enzy genf inpo inpr invt
## 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1
## lbtr medd mnob mobd neop tisu
## 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Quartan malaria
## semtypes
## dsyn humn popg hlca aggp anim grup inpr invt lbpr mamm orch patf phsu topp
## 5 5 4 2 1 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Anemia ----------> Simian malaria
## semtypes
## gngm aapp imft phsu humn bacs dsyn orch topp bpoc celc neop popg anim bird carb
## 6 5 5 5 4 3 3 3 3 2 2 2 2 1 1 1
## chem chvs elii emst grup hlca lbpr mamm patf prog
## 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Trait ----------> Algid malaria
## semtypes
## dsyn orch patf
## 2 1 1
##
## Sickle Cell Trait ----------> Congenital malaria
## semtypes
## humn dsyn popg anim aggp famg fndg orgm patf bacs imft inpr invt mamm orgf phsu
## 9 6 6 4 3 2 2 2 2 1 1 1 1 1 1 1
## sosy
## 1
##
## Sickle Cell Trait ----------> Induced malaria
## semtypes
## humn bpoc gngm popg aapp aggp anim bacs dsyn inpr mamm ortf prog
## 3 2 2 2 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Trait ----------> MALARIA RELAPSE
## semtypes
## humn popg
## 1 1
##
## Sickle Cell Trait ----------> Malaria
## integer(0)
##
## Sickle Cell Trait ----------> Malaria, Avian
## semtypes
## dsyn fndg bpoc celc chvf genf humn invt neop orgf orgm popg sosy
## 3 2 1 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Trait ----------> Malaria, Cerebral
## semtypes
## dsyn humn patf fndg bacs gngm topp aapp bpoc phsu popg sosy aggp hlca imft orch
## 33 16 14 12 10 9 9 8 8 8 8 6 5 5 4 4
## orgf anim comd famg inpo medd orgm acab chvf lbpr mobd npop carb celc genf inch
## 4 3 3 3 3 3 3 2 2 2 2 2 1 1 1 1
## inpr invt lipd mamm neop ortf prog resd vita
## 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Trait ----------> Malaria, Falciparum
## integer(0)
##
## Sickle Cell Trait ----------> Malaria, Vivax
## semtypes
## dsyn humn popg patf hlca aggp bacs fndg gngm orgf topp phsu aapp anim bpoc imft
## 25 25 16 11 7 5 5 5 5 5 5 4 3 3 3 3
## inpo sosy comd famg orch orgm prog bird blor celc chvf clna diap edac genf grup
## 3 3 2 2 2 2 2 1 1 1 1 1 1 1 1 1
## inch inpr invt lbpr lbtr mbrt mnob mobd neop npop ortf podg virs
## 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Trait ----------> Malarial hepatitis
## semtypes
## dsyn comd humn inpo patf popg
## 3 1 1 1 1 1
##
## Sickle Cell Trait ----------> Malarial nephrosis
## semtypes
## aggp humn inpr popg
## 1 1 1 1
##
## Sickle Cell Trait ----------> Mixed malaria
## semtypes
## humn popg aggp bird dsyn inpr orgf
## 2 2 1 1 1 1 1
##
## Sickle Cell Trait ----------> Ovale malaria
## semtypes
## humn popg dsyn aggp anim inpr invt orgm patf prog
## 7 5 4 2 1 1 1 1 1 1
##
## Sickle Cell Trait ----------> Plasmodium malariae
## semtypes
## dsyn invt orgm
## 6 1 1
##
## Sickle Cell Trait ----------> Plasmodium malariae infection
## semtypes
## humn dsyn popg patf aggp topp gngm hlca aapp anim bacs bpoc celc clna comd fndg
## 9 8 7 4 3 3 2 2 1 1 1 1 1 1 1 1
## genf inpo inpr invt lbpr lbtr mbrt mnob neop orch orgm phsu sosy virs
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Trait ----------> Quartan malaria
## semtypes
## humn popg dsyn aggp anim grup hlca inpr invt mamm orgm topp
## 5 4 3 1 1 1 1 1 1 1 1 1
##
## Sickle Cell Trait ----------> Simian malaria
## semtypes
## humn popg bird bpoc celc dsyn gngm invt neop orgm prog topp
## 3 2 1 1 1 1 1 1 1 1 1 1
##
## sickle trait ----------> Algid malaria
## semtypes
## dsyn humn topp patf orch popg aggp anim famg hlca fndg inpr orgm grup medd prog
## 66 45 34 26 23 22 15 14 12 11 5 5 5 3 2 2
## aapp bacs gngm neop
## 1 1 1 1
##
## sickle trait ----------> Congenital malaria
## semtypes
## humn aggp anim popg dsyn famg inpr orgm
## 5 3 3 3 2 1 1 1
##
## sickle trait ----------> Induced malaria
## semtypes
## aggp humn inpr popg
## 1 1 1 1
##
## sickle trait ----------> MALARIA RELAPSE
## semtypes
## hlca humn phsu orch popg dsyn topp resa aggp anim mamm famg imft fndg inpr grup
## 58 58 47 40 36 34 34 26 18 16 14 12 9 8 8 4
## orgm patf invt neop diap aapp bacs bdsu gngm prog tisu
## 4 4 3 3 2 1 1 1 1 1 1
##
## sickle trait ----------> Malaria
## semtypes
## humn dsyn popg famg aggp anim hlca fndg aapp bacs gngm grup inpr orgm patf topp
## 13 7 7 4 3 3 3 2 1 1 1 1 1 1 1 1
##
## sickle trait ----------> Malaria, Avian
## semtypes
## dsyn humn popg phsu aapp gngm famg fndg aggp anim hlca topp orch lbpr elii bpoc
## 416 367 188 121 120 112 100 97 94 94 74 68 61 56 54 47
## genf tisu neop inch inpr enzy orgm bdsu orgf patf bacs celc imft grup mbrt nusq
## 46 44 41 37 36 34 34 33 33 32 31 29 28 26 23 23
## sosy prog cell chvf medd invt orga bird npop
## 19 16 15 11 11 10 10 8 6
##
## sickle trait ----------> Malaria, Cerebral
## semtypes
## humn aggp dsyn popg anim fndg hlca inpr patf topp
## 3 2 2 2 1 1 1 1 1 1
##
## sickle trait ----------> Malaria, Falciparum
## integer(0)
##
## sickle trait ----------> Malaria, Vivax
## semtypes
## humn dsyn popg aggp anim famg hlca inpr orgm topp
## 6 4 4 3 3 1 1 1 1 1
##
## sickle trait ----------> Malarial hepatitis
## semtypes
## dsyn humn popg hlca patf comd inpo diap aggp anim famg fndg inpr topp grup orgm
## 112 80 52 32 30 27 27 20 17 17 16 7 6 6 5 5
## neop aapp bacs bdsu gngm medd prog tisu
## 3 2 2 2 2 2 2 2
##
## sickle trait ----------> Malarial nephrosis
## semtypes
## aggp humn inpr popg
## 1 1 1 1
##
## sickle trait ----------> Mixed malaria
## semtypes
## aggp humn inpr popg
## 1 1 1 1
##
## sickle trait ----------> Ovale malaria
## semtypes
## humn popg aggp anim dsyn inpr
## 2 2 1 1 1 1
##
## sickle trait ----------> Plasmodium malariae
## semtypes
## dsyn humn popg patf aggp anim famg hlca orgm inpr invt fndg topp grup neop prog
## 252 114 61 44 38 37 28 20 20 14 12 11 11 10 6 3
## aapp bacs gngm
## 1 1 1
##
## sickle trait ----------> Plasmodium malariae infection
## semtypes
## humn aggp popg anim inpr
## 3 2 2 1 1
##
## sickle trait ----------> Quartan malaria
## semtypes
## humn aggp grup hlca inpr popg topp
## 2 1 1 1 1 1 1
##
## sickle trait ----------> Simian malaria
## semtypes
## humn dsyn gngm popg aapp phsu imft anim topp hlca aggp bacs orch famg bpoc neop
## 290 264 147 144 118 108 102 97 78 74 65 65 65 63 57 47
## patf fndg mamm celc grup prog lbpr inpr orgm carb elii bdsu emst tisu cell orgt
## 41 38 38 34 32 31 29 24 23 19 19 13 13 13 10 9
## invt bird orgf chem chvs medd
## 8 6 6 2 2 2The summarize_predicates() function works similarly to
give information on predicate counts.
## Joining with `by = join_by(name)`
## Joining with `by = join_by(path_group, path_id)`
## Joining with `by = join_by(from, to, grand_path_id, name1, name2)`## Sickle Cell Anemia ----------> Algid malaria
## .
## CAUSES COEXISTS_WITH AFFECTS COMPLICATES ISA
## 4 3 1 1 1
## PREDISPOSES
## 1
##
## Sickle Cell Anemia ----------> Congenital malaria
## .
## PROCESS_OF COEXISTS_WITH AFFECTS OCCURS_IN
## 28 23 20 15
## CAUSES PREDISPOSES LOCATION_OF ASSOCIATED_WITH
## 8 7 6 4
## COMPLICATES ISA NEG_OCCURS_IN NEG_PROCESS_OF
## 3 3 3 3
## DIAGNOSES MANIFESTATION_OF NEG_COEXISTS_WITH PRODUCES
## 2 1 1 1
##
## Sickle Cell Anemia ----------> Induced malaria
## .
## ASSOCIATED_WITH PROCESS_OF AFFECTS TREATS
## 12 10 8 5
## COEXISTS_WITH PREVENTS LOCATION_OF OCCURS_IN
## 4 3 2 2
## PREDISPOSES CAUSES ISA NEG_COEXISTS_WITH
## 2 1 1 1
## NEG_OCCURS_IN NEG_PROCESS_OF
## 1 1
##
## Sickle Cell Anemia ----------> MALARIA RELAPSE
## .
## PROCESS_OF CAUSES NEG_TREATS PREVENTS TREATS
## 4 2 1 1 1
##
## Sickle Cell Anemia ----------> Malaria
## .
## CAUSES COEXISTS_WITH AFFECTS PREDISPOSES
## 2 2 1 1
##
## Sickle Cell Anemia ----------> Malaria, Avian
## .
## COEXISTS_WITH AFFECTS ASSOCIATED_WITH CAUSES
## 15 10 9 8
## TREATS LOCATION_OF PREDISPOSES ISA
## 8 7 3 2
## OCCURS_IN COMPLICATES MANIFESTATION_OF NEG_COEXISTS_WITH
## 2 1 1 1
## PRODUCES
## 1
##
## Sickle Cell Anemia ----------> Malaria, Cerebral
## .
## COEXISTS_WITH ASSOCIATED_WITH TREATS AFFECTS
## 243 164 150 136
## CAUSES PROCESS_OF PREDISPOSES PREVENTS
## 132 67 51 36
## OCCURS_IN LOCATION_OF DIAGNOSES AUGMENTS
## 32 31 29 24
## COMPLICATES ISA MANIFESTATION_OF PRODUCES
## 23 23 19 17
## DISRUPTS NEG_PROCESS_OF NEG_TREATS NEG_COEXISTS_WITH
## 15 10 7 6
## NEG_ASSOCIATED_WITH PRECEDES NEG_CAUSES NEG_AFFECTS
## 5 5 4 3
## NEG_PREDISPOSES NEG_DIAGNOSES NEG_LOCATION_OF NEG_OCCURS_IN
## 2 1 1 1
## PART_OF
## 1
##
## Sickle Cell Anemia ----------> Malaria, Falciparum
## .
## COEXISTS_WITH ASSOCIATED_WITH TREATS AFFECTS
## 296 216 194 142
## PROCESS_OF CAUSES OCCURS_IN DIAGNOSES
## 125 119 52 51
## PREDISPOSES COMPLICATES LOCATION_OF AUGMENTS
## 49 33 30 28
## PREVENTS ISA MANIFESTATION_OF PRODUCES
## 19 15 14 14
## NEG_TREATS DISRUPTS PRECEDES NEG_PROCESS_OF
## 13 12 8 7
## NEG_ASSOCIATED_WITH NEG_COEXISTS_WITH INTERACTS_WITH NEG_AFFECTS
## 5 4 2 2
## NEG_DIAGNOSES NEG_OCCURS_IN NEG_PREVENTS NEG_CAUSES
## 2 2 2 1
## NEG_DISRUPTS NEG_LOCATION_OF NEG_PREDISPOSES NEG_PRODUCES
## 1 1 1 1
##
## Sickle Cell Anemia ----------> Malaria, Vivax
## COEXISTS_WITH
## 1
##
## Sickle Cell Anemia ----------> Malarial hepatitis
## .
## COEXISTS_WITH CAUSES AFFECTS DIAGNOSES ISA
## 5 4 2 2 2
## PROCESS_OF PREDISPOSES
## 2 1
##
## Sickle Cell Anemia ----------> Malarial nephrosis
## .
## PROCESS_OF AFFECTS NEG_OCCURS_IN NEG_PROCESS_OF OCCURS_IN
## 2 1 1 1 1
##
## Sickle Cell Anemia ----------> Mixed malaria
## .
## COEXISTS_WITH AFFECTS PROCESS_OF CAUSES
## 7 6 6 5
## ASSOCIATED_WITH OCCURS_IN PREDISPOSES TREATS
## 4 2 2 2
## COMPLICATES ISA MANIFESTATION_OF NEG_COEXISTS_WITH
## 1 1 1 1
## NEG_OCCURS_IN NEG_PROCESS_OF
## 1 1
##
## Sickle Cell Anemia ----------> Ovale malaria
## .
## PROCESS_OF AFFECTS COEXISTS_WITH ASSOCIATED_WITH
## 16 8 8 6
## CAUSES OCCURS_IN NEG_PROCESS_OF TREATS
## 5 5 4 4
## PREDISPOSES NEG_OCCURS_IN COMPLICATES NEG_COEXISTS_WITH
## 3 2 1 1
##
## Sickle Cell Anemia ----------> Plasmodium malariae
## .
## COEXISTS_WITH CAUSES AFFECTS PROCESS_OF
## 13 12 5 5
## PREDISPOSES COMPLICATES INTERACTS_WITH MANIFESTATION_OF
## 3 2 1 1
## OCCURS_IN
## 1
##
## Sickle Cell Anemia ----------> Plasmodium malariae infection
## .
## COEXISTS_WITH PROCESS_OF AFFECTS TREATS
## 37 25 19 18
## CAUSES DIAGNOSES OCCURS_IN ASSOCIATED_WITH
## 16 10 10 8
## LOCATION_OF PREDISPOSES NEG_PROCESS_OF COMPLICATES
## 8 7 4 3
## AUGMENTS MANIFESTATION_OF NEG_DIAGNOSES NEG_TREATS
## 2 2 2 2
## ISA NEG_COEXISTS_WITH NEG_OCCURS_IN PRODUCES
## 1 1 1 1
##
## Sickle Cell Anemia ----------> Quartan malaria
## .
## COEXISTS_WITH PROCESS_OF AFFECTS CAUSES
## 13 12 8 7
## OCCURS_IN PREDISPOSES TREATS DIAGNOSES
## 5 4 3 2
## ISA PRECEDES PREVENTS COMPLICATES
## 2 2 2 1
## NEG_COEXISTS_WITH NEG_OCCURS_IN NEG_PROCESS_OF
## 1 1 1
##
## Sickle Cell Anemia ----------> Simian malaria
## .
## ASSOCIATED_WITH PROCESS_OF TREATS COEXISTS_WITH AFFECTS
## 12 11 10 8 5
## CAUSES LOCATION_OF PREDISPOSES DISRUPTS ISA
## 2 2 2 1 1
## PART_OF
## 1
##
## Sickle Cell Trait ----------> Algid malaria
## .
## COEXISTS_WITH CAUSES COMPLICATES ISA
## 4 3 1 1
## NEG_AFFECTS NEG_COEXISTS_WITH PREDISPOSES
## 1 1 1
##
## Sickle Cell Trait ----------> Congenital malaria
## .
## PROCESS_OF AFFECTS COEXISTS_WITH OCCURS_IN
## 18 11 8 8
## PREDISPOSES CAUSES ASSOCIATED_WITH ISA
## 6 5 2 2
## NEG_COEXISTS_WITH AUGMENTS NEG_AFFECTS NEG_OCCURS_IN
## 2 1 1 1
## NEG_PROCESS_OF
## 1
##
## Sickle Cell Trait ----------> Induced malaria
## .
## PROCESS_OF AFFECTS ASSOCIATED_WITH LOCATION_OF
## 8 2 2 2
##
## Sickle Cell Trait ----------> MALARIA RELAPSE
## PROCESS_OF
## 2
##
## Sickle Cell Trait ----------> Malaria
## .
## COEXISTS_WITH CAUSES NEG_AFFECTS NEG_COEXISTS_WITH
## 2 1 1 1
## PREDISPOSES
## 1
##
## Sickle Cell Trait ----------> Malaria, Avian
## .
## COEXISTS_WITH CAUSES AFFECTS LOCATION_OF
## 8 6 4 4
## PROCESS_OF ASSOCIATED_WITH NEG_AFFECTS NEG_COEXISTS_WITH
## 3 2 2 1
## OCCURS_IN PREDISPOSES TREATS
## 1 1 1
##
## Sickle Cell Trait ----------> Malaria, Cerebral
## .
## COEXISTS_WITH PROCESS_OF CAUSES AFFECTS
## 69 39 35 27
## ASSOCIATED_WITH TREATS COMPLICATES PREDISPOSES
## 27 15 12 11
## LOCATION_OF OCCURS_IN ISA AUGMENTS
## 10 10 8 5
## NEG_AFFECTS NEG_COEXISTS_WITH NEG_PROCESS_OF DIAGNOSES
## 5 4 4 3
## NEG_CAUSES PRECEDES PREVENTS PRODUCES
## 3 3 3 3
## MANIFESTATION_OF NEG_OCCURS_IN DISRUPTS NEG_ASSOCIATED_WITH
## 2 2 1 1
## NEG_PREDISPOSES
## 1
##
## Sickle Cell Trait ----------> Malaria, Falciparum
## PREDISPOSES
## 1
##
## Sickle Cell Trait ----------> Malaria, Vivax
## .
## COEXISTS_WITH PROCESS_OF CAUSES AFFECTS
## 59 53 26 22
## ASSOCIATED_WITH OCCURS_IN PREDISPOSES COMPLICATES
## 12 9 8 7
## ISA LOCATION_OF TREATS DIAGNOSES
## 6 6 6 4
## NEG_AFFECTS AUGMENTS MANIFESTATION_OF DISRUPTS
## 4 2 2 1
## NEG_COEXISTS_WITH NEG_OCCURS_IN NEG_PREDISPOSES NEG_PROCESS_OF
## 1 1 1 1
## PRECEDES PREVENTS
## 1 1
##
## Sickle Cell Trait ----------> Malarial hepatitis
## .
## COEXISTS_WITH CAUSES ISA PROCESS_OF
## 3 2 2 2
## AUGMENTS NEG_AFFECTS NEG_COEXISTS_WITH PREDISPOSES
## 1 1 1 1
##
## Sickle Cell Trait ----------> Malarial nephrosis
## .
## PROCESS_OF AFFECTS
## 2 1
##
## Sickle Cell Trait ----------> Mixed malaria
## .
## PROCESS_OF AFFECTS ASSOCIATED_WITH CAUSES
## 6 2 2 2
## COEXISTS_WITH NEG_AFFECTS NEG_COEXISTS_WITH PREDISPOSES
## 2 1 1 1
##
## Sickle Cell Trait ----------> Ovale malaria
## .
## PROCESS_OF COEXISTS_WITH AFFECTS CAUSES
## 15 7 4 3
## OCCURS_IN PREDISPOSES AUGMENTS COMPLICATES
## 3 2 1 1
## NEG_AFFECTS NEG_COEXISTS_WITH NEG_PROCESS_OF
## 1 1 1
##
## Sickle Cell Trait ----------> Plasmodium malariae
## .
## CAUSES COEXISTS_WITH PROCESS_OF AFFECTS
## 7 5 3 2
## NEG_COEXISTS_WITH AUGMENTS INTERACTS_WITH ISA
## 2 1 1 1
## NEG_AFFECTS PREDISPOSES
## 1 1
##
## Sickle Cell Trait ----------> Plasmodium malariae infection
## .
## PROCESS_OF COEXISTS_WITH CAUSES AFFECTS
## 19 15 8 7
## OCCURS_IN AUGMENTS DIAGNOSES LOCATION_OF
## 6 2 2 2
## NEG_COEXISTS_WITH PREDISPOSES TREATS ISA
## 2 2 2 1
## MANIFESTATION_OF NEG_AFFECTS NEG_PROCESS_OF
## 1 1 1
##
## Sickle Cell Trait ----------> Quartan malaria
## .
## PROCESS_OF COEXISTS_WITH CAUSES AFFECTS
## 13 5 4 2
## NEG_COEXISTS_WITH PREDISPOSES ISA NEG_AFFECTS
## 2 2 1 1
## OCCURS_IN PRECEDES PREVENTS
## 1 1 1
##
## Sickle Cell Trait ----------> Simian malaria
## .
## PROCESS_OF COEXISTS_WITH AFFECTS CAUSES
## 9 4 2 2
## LOCATION_OF NEG_AFFECTS TREATS ISA
## 2 2 2 1
## NEG_COEXISTS_WITH PREDISPOSES
## 1 1
##
## sickle trait ----------> Algid malaria
## .
## PROCESS_OF TREATS CAUSES COEXISTS_WITH
## 69 69 42 29
## ISA COMPLICATES AFFECTS OCCURS_IN
## 27 24 23 14
## ADMINISTERED_TO PREDISPOSES NEG_PROCESS_OF PREVENTS
## 10 10 9 7
## METHOD_OF ASSOCIATED_WITH NEG_ADMINISTERED_TO NEG_AFFECTS
## 6 4 4 3
## NEG_TREATS AUGMENTS NEG_OCCURS_IN NEG_PREVENTS
## 3 2 2 2
## DIAGNOSES MANIFESTATION_OF NEG_COEXISTS_WITH NEG_PREDISPOSES
## 1 1 1 1
## PRECEDES USES
## 1 1
##
## sickle trait ----------> Congenital malaria
## .
## PROCESS_OF OCCURS_IN AFFECTS COEXISTS_WITH NEG_OCCURS_IN
## 9 4 3 2 1
##
## sickle trait ----------> Induced malaria
## PROCESS_OF
## 2
##
## sickle trait ----------> MALARIA RELAPSE
## .
## PROCESS_OF TREATS PREVENTS ADMINISTERED_TO
## 85 79 53 37
## COEXISTS_WITH CAUSES PREDISPOSES DISRUPTS
## 30 27 11 10
## NEG_ADMINISTERED_TO USES NEG_TREATS AFFECTS
## 9 9 8 7
## ISA PART_OF compared_with METHOD_OF
## 6 6 5 4
## PRECEDES higher_than lower_than same_as
## 2 2 2 2
## ASSOCIATED_WITH AUGMENTS INTERACTS_WITH LOCATION_OF
## 1 1 1 1
## NEG_METHOD_OF NEG_PROCESS_OF NEG_USES NEG_lower_than
## 1 1 1 1
## OCCURS_IN
## 1
##
## sickle trait ----------> Malaria
## .
## PROCESS_OF COEXISTS_WITH AFFECTS OCCURS_IN CAUSES
## 26 14 12 10 8
## NEG_PROCESS_OF PREDISPOSES TREATS ASSOCIATED_WITH PREVENTS
## 6 6 6 4 3
## AUGMENTS NEG_AFFECTS NEG_OCCURS_IN NEG_TREATS ISA
## 2 2 2 2 1
## NEG_PREDISPOSES NEG_PREVENTS
## 1 1
##
## sickle trait ----------> Malaria, Avian
## .
## PROCESS_OF COEXISTS_WITH TREATS LOCATION_OF
## 544 312 229 208
## AFFECTS ASSOCIATED_WITH CAUSES PART_OF
## 168 148 142 140
## PREDISPOSES ISA OCCURS_IN NEG_PROCESS_OF
## 104 88 79 68
## ADMINISTERED_TO NEG_LOCATION_OF PREVENTS NEG_PART_OF
## 46 20 20 17
## USES NEG_AFFECTS NEG_TREATS DIAGNOSES
## 16 15 15 9
## METHOD_OF PRODUCES NEG_ASSOCIATED_WITH AUGMENTS
## 8 8 7 6
## INTERACTS_WITH NEG_OCCURS_IN compared_with COMPLICATES
## 5 5 5 4
## NEG_PREDISPOSES MANIFESTATION_OF NEG_ADMINISTERED_TO NEG_COEXISTS_WITH
## 4 3 3 3
## STIMULATES NEG_PREVENTS higher_than DISRUPTS
## 3 2 2 1
## NEG_CAUSES PRECEDES lower_than same_as
## 1 1 1 1
##
## sickle trait ----------> Malaria, Cerebral
## .
## COEXISTS_WITH PROCESS_OF CAUSES AFFECTS TREATS
## 6 6 3 2 2
## COMPLICATES ISA NEG_OCCURS_IN NEG_PROCESS_OF OCCURS_IN
## 1 1 1 1 1
## PRECEDES PREDISPOSES PREVENTS
## 1 1 1
##
## sickle trait ----------> Malaria, Falciparum
## COEXISTS_WITH
## 1
##
## sickle trait ----------> Malaria, Vivax
## .
## PROCESS_OF COEXISTS_WITH TREATS AFFECTS CAUSES
## 12 6 2 1 1
## NEG_PROCESS_OF OCCURS_IN PRECEDES PREDISPOSES PREVENTS
## 1 1 1 1 1
##
## sickle trait ----------> Malarial hepatitis
## .
## PROCESS_OF COEXISTS_WITH CAUSES ISA
## 133 68 56 29
## TREATS DIAGNOSES OCCURS_IN AFFECTS
## 28 27 26 22
## PREDISPOSES NEG_PROCESS_OF ADMINISTERED_TO INTERACTS_WITH
## 19 17 12 11
## ASSOCIATED_WITH PREVENTS NEG_ADMINISTERED_TO NEG_OCCURS_IN
## 6 6 5 5
## NEG_TREATS AUGMENTS COMPLICATES NEG_AFFECTS
## 4 3 3 3
## USES MANIFESTATION_OF METHOD_OF NEG_INTERACTS_WITH
## 3 2 2 2
## NEG_PREVENTS PART_OF PRECEDES LOCATION_OF
## 2 2 2 1
## NEG_COEXISTS_WITH NEG_PREDISPOSES NEG_USES
## 1 1 1
##
## sickle trait ----------> Malarial nephrosis
## PROCESS_OF
## 2
##
## sickle trait ----------> Mixed malaria
## PROCESS_OF
## 2
##
## sickle trait ----------> Ovale malaria
## .
## PROCESS_OF COEXISTS_WITH
## 4 2
##
## sickle trait ----------> Plasmodium malariae
## .
## PROCESS_OF CAUSES COEXISTS_WITH AFFECTS
## 329 205 117 66
## OCCURS_IN TREATS PREDISPOSES NEG_PROCESS_OF
## 53 39 34 32
## PREVENTS COMPLICATES INTERACTS_WITH ISA
## 13 11 10 9
## NEG_AFFECTS PRECEDES ASSOCIATED_WITH AUGMENTS
## 6 5 4 4
## NEG_OCCURS_IN MANIFESTATION_OF NEG_CAUSES NEG_TREATS
## 4 3 3 3
## NEG_PREDISPOSES NEG_PREVENTS NEG_COEXISTS_WITH
## 2 2 1
##
## sickle trait ----------> Plasmodium malariae infection
## .
## PROCESS_OF OCCURS_IN
## 6 2
##
## sickle trait ----------> Quartan malaria
## .
## PROCESS_OF OCCURS_IN PREVENTS TREATS
## 4 1 1 1
##
## sickle trait ----------> Simian malaria
## .
## PROCESS_OF COEXISTS_WITH TREATS ASSOCIATED_WITH
## 470 247 217 190
## LOCATION_OF AFFECTS PART_OF ADMINISTERED_TO
## 112 88 80 64
## PREDISPOSES ISA CAUSES INTERACTS_WITH
## 62 44 42 42
## NEG_PROCESS_OF OCCURS_IN DISRUPTS PREVENTS
## 33 29 19 16
## USES NEG_ADMINISTERED_TO NEG_LOCATION_OF NEG_TREATS
## 13 11 11 11
## NEG_PART_OF AUGMENTS METHOD_OF NEG_INTERACTS_WITH
## 8 6 5 5
## PRODUCES NEG_AFFECTS NEG_ASSOCIATED_WITH compared_with
## 5 4 4 4
## DIAGNOSES NEG_OCCURS_IN NEG_PREVENTS PRECEDES
## 2 2 2 2
## STIMULATES same_as COMPLICATES NEG_METHOD_OF
## 2 2 1 1
## NEG_PREDISPOSES NEG_PRODUCES NEG_USES higher_than
## 1 1 1 1
## lower_than
## 1## # A tibble: 51 × 3
## # Groups: from [3]
## from to predicates
## <chr> <chr> <list>
## 1 Sickle Cell Anemia Algid malaria <table [6]>
## 2 Sickle Cell Anemia Congenital malaria <table [16]>
## 3 Sickle Cell Anemia Induced malaria <table [14]>
## 4 Sickle Cell Anemia MALARIA RELAPSE <table [5]>
## 5 Sickle Cell Anemia Malaria <table [4]>
## 6 Sickle Cell Anemia Malaria, Avian <table [13]>
## 7 Sickle Cell Anemia Malaria, Cerebral <table [29]>
## 8 Sickle Cell Anemia Malaria, Falciparum <table [32]>
## 9 Sickle Cell Anemia Malaria, Vivax <int [1]>
## 10 Sickle Cell Anemia Malarial hepatitis <table [7]>
## # ℹ 41 more rows## .
## CAUSES COEXISTS_WITH AFFECTS COMPLICATES ISA
## 4 3 1 1 1
## PREDISPOSES
## 1Aim 2: Expanding a single node set
Another way in which we can explore relations between ideas is to
slowly expand a single set of ideas to see what other ideas are
connected. We can do this with the grow_nodes() function.
The grow_nodes() function takes a set of nodes and obtains
the nodes that are directly connected to any of these nodes. That is, it
obtains the set of nodes that are distance 1 away from the supplied
nodes. We can call this set of nodes the “1-neighborhood” of the
supplied nodes.
## + 2/1038 vertices, named, from f0c97f6:
## [1] Sickle Cell Trait sickle trait## + 476/1038 vertices, named, from f0c97f6:
## [1] Infant
## [2] Control Groups
## [3] Mus
## [4] Diabetic
## [5] Obstruction
## [6] Carcinoma
## [7] Neoplasm
## [8] Nitric Oxide
## [9] Renal tubular disorder
## [10] Brain
## + ... omitted several verticesNot all nodes in the 1-neighborhood will be useful, and we may wish
to remove them with find_nodes(..., match = FALSE). We can
use summarize_semtypes() to begin to identify such nodes.
Using the argument is_path = FALSE will change the format
of the display and output to better suit this situation.
## Joining with `by = join_by(name)`
## Joining with `by = join_by(name)`
## Adding missing grouping variables: `semtype`## bacs --------------------
## # A tibble: 32 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 bacs Glucose 3165 99.7
## 2 bacs Nitric Oxide 2961 99.4
## 3 bacs Antigens 2450 98.3
## 4 bacs Hemoglobin 2197 97.4
## 5 bacs Insulin-Like Growth Factor I 1782 93.6
## 6 bacs Lipoproteins 1415 88.2
## 7 bacs Phospholipids 1346 86.4
## 8 bacs Cell Adhesion Molecules 1306 85.3
## 9 bacs Family 1299 85.1
## 10 bacs Vitamin A 1023 79.1
## # ℹ 22 more rows## Adding missing grouping variables: `semtype`## bpoc --------------------
## # A tibble: 11 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 bpoc Glucose 3165 99.7
## 2 bpoc Male population group 1466 89.3
## 3 bpoc Brain 1422 88.4
## 4 bpoc Heart 1189 83.0
## 5 bpoc Spleen 940 77.0
## 6 bpoc Eye 877 75.0
## 7 bpoc Renal tubular disorder 814 72.5
## 8 bpoc Chamber 431 52.7
## 9 bpoc Splenomegaly 341 46.1
## 10 bpoc High pressure liquid chromatography pro… 262 38.5
## 11 bpoc Anterior chamber of eye structure 20 4.24## Adding missing grouping variables: `semtype`## carb --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 carb Glucose 3165 99.7## Adding missing grouping variables: `semtype`## hlca --------------------
## # A tibble: 31 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 hlca Glucose 3165 99.7
## 2 hlca Operative Surgical Procedures 2379 98.1
## 3 hlca Hemoglobin 2197 97.4
## 4 hlca Prophylactic treatment 1671 92.4
## 5 hlca Detection 1587 91.4
## 6 hlca Epinephrine 1451 89.1
## 7 hlca FAVOR 1383 87.0
## 8 hlca Anesthetics 870 75.0
## 9 hlca follow-up 865 74.7
## 10 hlca Screening procedure 828 73.0
## # ℹ 21 more rows## Adding missing grouping variables: `semtype`## phsu --------------------
## # A tibble: 16 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 phsu Glucose 3165 99.7
## 2 phsu Antigens 2450 98.3
## 3 phsu Hemoglobin 2197 97.4
## 4 phsu Immunoglobulins 1509 90.0
## 5 phsu Epinephrine 1451 89.1
## 6 phsu Lipoproteins 1415 88.2
## 7 phsu FAVOR 1383 87.0
## 8 phsu Vitamin A 1023 79.1
## 9 phsu Anesthetics 870 75.0
## 10 phsu Protective Agents 692 67.5
## 11 phsu Phosphorus 593 62.1
## 12 phsu Thioctic Acid 555 59.8
## 13 phsu PTPN11 gene|PTPN11 512 57.8
## 14 phsu Polymerase Chain Reaction 479 56.3
## 15 phsu Hepatitis B Surface Antigens 463 55.5
## 16 phsu High pressure liquid chromatography pro… 262 38.5## Adding missing grouping variables: `semtype`## dsyn --------------------
## # A tibble: 173 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 dsyn Neoplasm 3141 99.6
## 2 dsyn Hypertensive disease 2989 99.5
## 3 dsyn Diabetes 2925 99.3
## 4 dsyn Injury 2765 98.9
## 5 dsyn Cerebrovascular accident 2399 98.2
## 6 dsyn Operative Surgical Procedures 2379 98.1
## 7 dsyn Septicemia 2197 97.4
## 8 dsyn Atherosclerosis 2153 96.9
## 9 dsyn Oxidative Stress 2120 96.7
## 10 dsyn Obstruction 2074 96.5
## # ℹ 163 more rows## Adding missing grouping variables: `semtype`## fndg --------------------
## # A tibble: 57 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 fndg "Neoplasm" 3141 99.6
## 2 fndg "Hypertensive disease" 2989 99.5
## 3 fndg "Growth" 2162 97.1
## 4 fndg "Lesion" 1690 92.5
## 5 fndg "Pre-Eclampsia" 1549 90.8
## 6 fndg "Pain" 1378 86.8
## 7 fndg "Little\\s Disease" 1337 86.0
## 8 fndg "Neoplasm Metastasis" 1303 85.2
## 9 fndg "Systemic arterial pressure" 1235 84.1
## 10 fndg "Carcinoma" 1123 81.4
## # ℹ 47 more rows## Adding missing grouping variables: `semtype`## lbpr --------------------
## # A tibble: 19 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 lbpr Neoplasm 3141 99.6
## 2 lbpr Immunoglobulins 1509 90.0
## 3 lbpr Racial group 611 63.1
## 4 lbpr PTPN11 gene|PTPN11 512 57.8
## 5 lbpr Polymerase Chain Reaction 479 56.3
## 6 lbpr CALCULATION 291 41.7
## 7 lbpr High pressure liquid chromatography pro… 262 38.5
## 8 lbpr Chromatography 170 28.2
## 9 lbpr Fractionation 157 26.2
## 10 lbpr Genetic screening method 131 23.7
## 11 lbpr Isoelectric Focusing 94 18.3
## 12 lbpr Serum iron 73 14.4
## 13 lbpr Blood Cell Count 63 13.1
## 14 lbpr Hemoglobin electrophoresis 31 6.17
## 15 lbpr Blood investigation 27 5.39
## 16 lbpr Hemoglobin A2 measurement 24 5.01
## 17 lbpr SOLUBILITY TEST 12 2.89
## 18 lbpr Urine drug screening 8 2.02
## 19 lbpr Sickling test 4 0.771## Adding missing grouping variables: `semtype`## neop --------------------
## # A tibble: 14 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 neop "Neoplasm" 3141 99.6
## 2 neop "Neoplasm Metastasis" 1303 85.2
## 3 neop "Carcinoma" 1123 81.4
## 4 neop "host" 877 75.0
## 5 neop "Multiple Myeloma" 868 74.8
## 6 neop "Renal Cell Carcinoma" 830 73.3
## 7 neop "Osteogenesis" 588 61.7
## 8 neop "Retinoblastoma" 338 45.8
## 9 neop "Burkitt Lymphoma" 246 36.5
## 10 neop "Medullary carcinoma" 184 29.0
## 11 neop "Histiocytoma, Malignant Fibrous" 156 25.9
## 12 neop "[M]Neoplasm morphology NOS" 59 11.9
## 13 neop "African Burkitt\\s lymphoma" 56 11.2
## 14 neop "Collecting Duct Carcinoma (Kidney)" 44 8.77## Adding missing grouping variables: `semtype`## sosy --------------------
## # A tibble: 24 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 sosy Neoplasm 3141 99.6
## 2 sosy Hypertensive disease 2989 99.5
## 3 sosy Asthma 2036 96.0
## 4 sosy Ischemia 2034 95.9
## 5 sosy Heart failure 1859 94.7
## 6 sosy Ischemic stroke 1406 87.9
## 7 sosy Pain 1378 86.8
## 8 sosy Thrombus 1352 86.5
## 9 sosy Neoplasm Metastasis 1303 85.2
## 10 sosy Fever 1087 80.6
## # ℹ 14 more rows## Adding missing grouping variables: `semtype`## inch --------------------
## # A tibble: 3 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 inch Nitric Oxide 2961 99.4
## 2 inch Bicarbonates 629 64.5
## 3 inch WATER,DISTILLED 213 33.0## Adding missing grouping variables: `semtype`## topp --------------------
## # A tibble: 35 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 topp Nitric Oxide 2961 99.4
## 2 topp Interleukin-1 beta 2718 98.8
## 3 topp Operative Surgical Procedures 2379 98.1
## 4 topp Hemoglobin 2197 97.4
## 5 topp Insulin-Like Growth Factor I 1782 93.6
## 6 topp Prophylactic treatment 1671 92.4
## 7 topp Detection 1587 91.4
## 8 topp Immunoglobulins 1509 90.0
## 9 topp Epinephrine 1451 89.1
## 10 topp Heart 1189 83.0
## # ℹ 25 more rows## Adding missing grouping variables: `semtype`## comd --------------------
## # A tibble: 3 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 comd Injury 2765 98.9
## 2 comd Oxidative Stress 2120 96.7
## 3 comd Necrosis 1532 90.4## Adding missing grouping variables: `semtype`## inpo --------------------
## # A tibble: 14 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 inpo Injury 2765 98.9
## 2 inpo Oxidative Stress 2120 96.7
## 3 inpo Necrosis 1532 90.4
## 4 inpo Wounds and Injuries 1114 81.3
## 5 inpo Ulcer 987 78.7
## 6 inpo Suicide 338 45.8
## 7 inpo Compartment syndromes 255 38.0
## 8 inpo Thermal injury 249 37.2
## 9 inpo Heat Stroke 212 32.9
## 10 inpo Laceration 193 30.2
## 11 inpo Splenic Rupture 85 16.6
## 12 inpo Heat exposure 60 12.5
## 13 inpo Heat Illness 40 8.19
## 14 inpo Traumatic hyphema 27 5.39## Adding missing grouping variables: `semtype`## patf --------------------
## # A tibble: 51 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 patf Injury 2765 98.9
## 2 patf Septicemia 2197 97.4
## 3 patf Oxidative Stress 2120 96.7
## 4 patf Obstruction 2074 96.5
## 5 patf Asthma 2036 96.0
## 6 patf Hypoxia 1935 95.4
## 7 patf Kidney Failure, Chronic 1738 93.1
## 8 patf Tuberculosis 1602 91.8
## 9 patf Shock 1570 91.1
## 10 patf Necrosis 1532 90.4
## # ℹ 41 more rows## Adding missing grouping variables: `semtype`## aapp --------------------
## # A tibble: 40 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 aapp Interleukin-1 beta 2718 98.8
## 2 aapp Hemoglobin 2197 97.4
## 3 aapp Lipids 2175 97.3
## 4 aapp Insulin-Like Growth Factor I 1782 93.6
## 5 aapp Immunoglobulins 1509 90.0
## 6 aapp Lipoproteins 1415 88.2
## 7 aapp Cell Adhesion Molecules 1306 85.3
## 8 aapp Neoplasm Metastasis 1303 85.2
## 9 aapp Family 1299 85.1
## 10 aapp End stage renal failure 1112 81.1
## # ℹ 30 more rows## Adding missing grouping variables: `semtype`## gngm --------------------
## # A tibble: 41 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 gngm Interleukin-1 beta 2718 98.8
## 2 gngm Hemoglobin 2197 97.4
## 3 gngm Insulin-Like Growth Factor I 1782 93.6
## 4 gngm Immunoglobulins 1509 90.0
## 5 gngm Male population group 1466 89.3
## 6 gngm Lipoproteins 1415 88.2
## 7 gngm Cell Adhesion Molecules 1306 85.3
## 8 gngm Family 1299 85.1
## 9 gngm End stage renal failure 1112 81.1
## 10 gngm Serum Proteins 789 71.6
## # ℹ 31 more rows## Adding missing grouping variables: `semtype`## imft --------------------
## # A tibble: 8 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 imft Interleukin-1 beta 2718 98.8
## 2 imft Antigens 2450 98.3
## 3 imft Immunoglobulins 1509 90.0
## 4 imft Protective Agents 692 67.5
## 5 imft Hepatitis B Surface Antigens 463 55.5
## 6 imft L-Selectin 427 52.5
## 7 imft Soluble antigen 270 39.0
## 8 imft Merozoite Surface Protein 1|MSMB|MST1|TM… 44 8.77## Adding missing grouping variables: `semtype`## aggp --------------------
## # A tibble: 8 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 aggp Child 2336 98.0
## 2 aggp Adult 1708 92.7
## 3 aggp Infant 1567 91.0
## 4 aggp Infant, Newborn 1336 85.9
## 5 aggp Adolescent 1160 82.3
## 6 aggp Young adult 846 73.8
## 7 aggp School child 398 50.5
## 8 aggp Adolescents, Female 305 42.9## Adding missing grouping variables: `semtype`## humn --------------------
## # A tibble: 79 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 humn Child 2336 98.0
## 2 humn Woman 1984 95.6
## 3 humn Adult 1708 92.7
## 4 humn Infant 1567 91.0
## 5 humn Control Groups 1474 89.5
## 6 humn Male population group 1466 89.3
## 7 humn cohort 1383 87.0
## 8 humn Mothers 1362 86.6
## 9 humn Infant, Newborn 1336 85.9
## 10 humn Family 1299 85.1
## # ℹ 69 more rows## Adding missing grouping variables: `semtype`## inpr --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 inpr Child 2336 98.0## Adding missing grouping variables: `semtype`## popg --------------------
## # A tibble: 41 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 popg Child 2336 98.0
## 2 popg Woman 1984 95.6
## 3 popg Male population group 1466 89.3
## 4 popg cohort 1383 87.0
## 5 popg Mothers 1362 86.6
## 6 popg Participant 1293 84.9
## 7 popg Pregnant Women 1082 80.4
## 8 popg Boys 908 76.4
## 9 popg Girls 901 76.0
## 10 popg Caucasoid Race 786 71.3
## # ℹ 31 more rows## Adding missing grouping variables: `semtype`## lipd --------------------
## # A tibble: 3 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 lipd Lipids 2175 97.3
## 2 lipd Phospholipids 1346 86.4
## 3 lipd High Density Lipoprotein Cholesterol 829 73.1## Adding missing grouping variables: `semtype`## npop --------------------
## # A tibble: 8 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 npop Growth 2162 97.1
## 2 npop Radiation 478 56.2
## 3 npop density 419 51.9
## 4 npop High pressure liquid chromatography proc… 262 38.5
## 5 npop Male gender 248 37.0
## 6 npop Humidity 95 18.5
## 7 npop Weather 92 17.8
## 8 npop Physical Development 50 10.4## Adding missing grouping variables: `semtype`## orgf --------------------
## # A tibble: 8 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 orgf Growth 2162 97.1
## 2 orgf Cessation of life 1781 93.5
## 3 orgf Pregnancy 1422 88.4
## 4 orgf Recovery 1234 84.0
## 5 orgf Premature Birth 1015 79.0
## 6 orgf Question of pregnancy 761 70.4
## 7 orgf Fertility 673 67.1
## 8 orgf Osteogenesis 588 61.7## Adding missing grouping variables: `semtype`## acab --------------------
## # A tibble: 6 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 acab Atherosclerosis 2153 96.9
## 2 acab Lesion 1690 92.5
## 3 acab Reconstructive Surgical Procedures 759 70.3
## 4 acab Vascular occlusion 380 49.4
## 5 acab Aneurysm, Dissecting 272 39.1
## 6 acab Cilioretinal artery occlusion 6 1.45## Adding missing grouping variables: `semtype`## anim --------------------
## # A tibble: 6 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 anim Woman 1984 95.6
## 2 anim Mus 1423 88.6
## 3 anim Mothers 1362 86.6
## 4 anim Infant, Newborn 1336 85.9
## 5 anim Animal Model 712 68.7
## 6 anim Mice, Transgenic 565 60.7## Adding missing grouping variables: `semtype`## orch --------------------
## # A tibble: 8 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 orch Shock 1570 91.1
## 2 orch Epinephrine 1451 89.1
## 3 orch FAVOR 1383 87.0
## 4 orch Vitamin A 1023 79.1
## 5 orch Heme 754 70.0
## 6 orch Thioctic Acid 555 59.8
## 7 orch Polymerase Chain Reaction 479 56.3
## 8 orch High pressure liquid chromatography proc… 262 38.5## Adding missing grouping variables: `semtype`## grup --------------------
## # A tibble: 3 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 grup Control Groups 1474 89.5
## 2 grup Donor person 854 74.1
## 3 grup Focus Groups 147 25.0## Adding missing grouping variables: `semtype`## horm --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 horm Epinephrine 1451 89.1## Adding missing grouping variables: `semtype`## nsba --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 nsba Epinephrine 1451 89.1## Adding missing grouping variables: `semtype`## mamm --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 mamm Mus 1423 88.6
## 2 mamm Mice, Transgenic 565 60.7## Adding missing grouping variables: `semtype`## ortf --------------------
## # A tibble: 7 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 ortf Brain 1422 88.4
## 2 ortf Baresthesia 951 77.5
## 3 ortf Osteogenesis 588 61.7
## 4 ortf Urinary concentration 254 37.9
## 5 ortf Nervous System Physiology 232 35.5
## 6 ortf Lung volume 186 29.3
## 7 ortf testicular function 125 23.0## Adding missing grouping variables: `semtype`## vita --------------------
## # A tibble: 3 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 vita FAVOR 1383 87.0
## 2 vita Vitamin A 1023 79.1
## 3 vita Thioctic Acid 555 59.8## Adding missing grouping variables: `semtype`## mobd --------------------
## # A tibble: 4 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 mobd Pain 1378 86.8
## 2 mobd Mental disorders 923 76.8
## 3 mobd Psychotic Disorders 577 61.3
## 4 mobd Abdominal Pain 365 47.7## Adding missing grouping variables: `semtype`## famg --------------------
## # A tibble: 14 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 famg Mothers 1362 86.6
## 2 famg Family 1299 85.1
## 3 famg parent 897 75.8
## 4 famg sibling 697 68.1
## 5 famg Family member 632 64.8
## 6 famg Couples 508 57.6
## 7 famg Relative (related person) 464 55.7
## 8 famg father 438 53.1
## 9 famg Brothers 326 44.9
## 10 famg Daughter 305 42.9
## 11 famg son 276 39.6
## 12 famg Monozygotic twins 227 34.7
## 13 famg descent 207 32.5
## 14 famg Expectant Parents 6 1.45## Adding missing grouping variables: `semtype`## orgm --------------------
## # A tibble: 5 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 orgm Infant, Newborn 1336 85.9
## 2 orgm Animal Model 712 68.7
## 3 orgm Parasites 317 44.2
## 4 orgm Heterozygote 286 41.1
## 5 orgm Homozygote 252 37.6## Adding missing grouping variables: `semtype`## celf --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 celf Mutation 1318 85.5## Adding missing grouping variables: `semtype`## genf --------------------
## # A tibble: 3 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 genf Mutation 1318 85.5
## 2 genf Polymorphism, Genetic 956 77.6
## 3 genf Polymerase Chain Reaction 479 56.3## Adding missing grouping variables: `semtype`## phsf --------------------
## # A tibble: 3 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 phsf Mutation 1318 85.5
## 2 phsf Fertility 673 67.1
## 3 phsf Energy Metabolism 660 66.3## Adding missing grouping variables: `semtype`## medd --------------------
## # A tibble: 9 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 medd Heart 1189 83.0
## 2 medd Baresthesia 951 77.5
## 3 medd Probes 556 60.0
## 4 medd Chamber 431 52.7
## 5 medd Prosthesis 322 44.7
## 6 medd Frame 281 40.1
## 7 medd High pressure liquid chromatography proc… 262 38.5
## 8 medd Tourniquets 122 22.8
## 9 medd COMPONENTS, EXERCISE 11 2.70## Adding missing grouping variables: `semtype`## chvf --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 chvf Agent 1066 80.2
## 2 chvf Protective Agents 692 67.5## Adding missing grouping variables: `semtype`## anab --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 anab Congenital Abnormality 1044 79.7
## 2 anab Abnormal shape 346 46.4## Adding missing grouping variables: `semtype`## cgab --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 cgab Congenital Abnormality 1044 79.7
## 2 cgab Polycystic Kidney, Autosomal Dominant 378 49.1## Adding missing grouping variables: `semtype`## prog --------------------
## # A tibble: 13 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 prog Voluntary Workers 987 78.7
## 2 prog Physicians 950 77.3
## 3 prog student 654 65.9
## 4 prog athlete 648 65.5
## 5 prog Health Personnel 623 64.1
## 6 prog Nurses 554 59.7
## 7 prog General Practitioners 408 51.4
## 8 prog Soldiers 310 43.6
## 9 prog Pilot 290 41.5
## 10 prog Counsel - legal 180 28.8
## 11 prog Provider 162 26.6
## 12 prog Policy Makers 132 23.9
## 13 prog Artist 60 12.5## Adding missing grouping variables: `semtype`## orga --------------------
## # A tibble: 14 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 orga Baresthesia 951 77.5
## 2 orga Streptococcus pneumoniae 360 46.9
## 3 orga Salmonella 288 41.3
## 4 orga Male gender 248 37.0
## 5 orga Ability 232 35.5
## 6 orga Gender 153 25.8
## 7 orga Sex Characteristics 117 22.1
## 8 orga sex 115 21.8
## 9 orga heart rate 114 21.5
## 10 orga Birth Weight 87 17.1
## 11 orga CROSS SECTION 59 11.9
## 12 orga Body Temperature 50 10.4
## 13 orga DIFFUSION CAPACITY 14 3.37
## 14 orga Propositus 6 1.45## Adding missing grouping variables: `semtype`## celc --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 celc host 877 75.0## Adding missing grouping variables: `semtype`## podg --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 podg Cancer Patient 869 74.9
## 2 podg Hospitalized Patients 596 62.2## Adding missing grouping variables: `semtype`## strd --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 strd High Density Lipoprotein Cholesterol 829 73.1## Adding missing grouping variables: `semtype`## resd --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 resd Animal Model 712 68.7## Adding missing grouping variables: `semtype`## diap --------------------
## # A tibble: 7 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 diap Biopsy 650 65.6
## 2 diap tomography 619 63.6
## 3 diap Differential Diagnosis 425 52.1
## 4 diap Electrocardiogram 375 48.9
## 5 diap Echocardiography, Transesophageal 173 28.4
## 6 diap Intravenous pyelogram 111 21.2
## 7 diap Velocity measurement 56 11.2## Adding missing grouping variables: `semtype`## mbrt --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 mbrt Racial group 611 63.1
## 2 mbrt Polymerase Chain Reaction 479 56.3## Adding missing grouping variables: `semtype`## elii --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 elii Phosphorus 593 62.1## Adding missing grouping variables: `semtype`## enzy --------------------
## # A tibble: 3 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 enzy Thioctic Acid 555 59.8
## 2 enzy Hydrolase 365 47.7
## 3 enzy Pyruvate Kinase 324 44.8## Adding missing grouping variables: `semtype`## dora --------------------
## # A tibble: 4 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 dora Exercise 506 57.2
## 2 dora Sports 164 27.2
## 3 dora Strenuous Exercise 58 11.7
## 4 dora Athletics 39 8.00## Adding missing grouping variables: `semtype`## clna --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 clna Polymerase Chain Reaction 479 56.3## Adding missing grouping variables: `semtype`## lbtr --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 lbtr Polymerase Chain Reaction 479 56.3## Adding missing grouping variables: `semtype`## mnob --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 mnob Polymerase Chain Reaction 479 56.3## Adding missing grouping variables: `semtype`## virs --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 virs Polymerase Chain Reaction 479 56.3## Adding missing grouping variables: `semtype`## cell --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 cell Phagocytes 469 55.9
## 2 cell Drepanocyte 162 26.6## Adding missing grouping variables: `semtype`## blor --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 blor Abdominal Pain 365 47.7
## 2 blor Fingers 111 21.2## Adding missing grouping variables: `semtype`## bact --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 bact Streptococcus pneumoniae 360 46.9
## 2 bact Salmonella 288 41.3## Adding missing grouping variables: `semtype`## bdsu --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 bdsu peripheral blood 358 46.8## Adding missing grouping variables: `semtype`## tisu --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 tisu peripheral blood 358 46.8## Adding missing grouping variables: `semtype`## invt --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 invt Parasites 317 44.2
## 2 invt Tetrameres 33 6.55## Adding missing grouping variables: `semtype`## nusq --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 nusq Frame 281 40.1## Adding missing grouping variables: `semtype`## eico --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 eico 8-isoprostaglandin F2alpha 214 33.2## Adding missing grouping variables: `semtype`## irda --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 irda 8-isoprostaglandin F2alpha 214 33.2## Adding missing grouping variables: `semtype`## food --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 food Fruit 141 24.7## Adding missing grouping variables: `semtype`## edac --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 edac Training 127 23.2## Adding missing grouping variables: `semtype`## bird --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 bird Meleagris gallopavo 107 20.4## Adding missing grouping variables: `semtype`## moft --------------------
## # A tibble: 2 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 moft Metabolic Control 91 17.5
## 2 moft cytochrome c oxidase activity 27 5.39## Adding missing grouping variables: `semtype`## fish --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 fish Lethrinidae 65 13.6## Adding missing grouping variables: `semtype`## plnt --------------------
## # A tibble: 1 × 4
## # Groups: semtype [1]
## semtype name node_degree node_degree_perc
## <chr> <chr> <dbl> <dbl>
## 1 plnt Bahia 20 4.24The printed summary displays nodes grouped by semantic type. The semantic types are ordered such that the semantic type with the highest degree node is shown first. Often, these high degree nodes are less interesting because they represent fairly broad concepts.
- The
node_degreecolumn shows the degree of the node in the Semantic MEDLINE graph. - The
node_degree_perccolumn gives the percentile of the node degree relative to all nodes in the Semantic MEDLINE graph.
The resulting tibble
(nbrs_sickle_trait_summ) contains the same information that
is printed and provides another way to mine for nodes to remove.
After inspection of the summary, we can remove nodes based on
semantic type and/or name. We can achieve this with
find_nodes(..., match = FALSE). The ... can be
any combination of the pattern, names, or
semtypes arguments. If a node matches any of these pieces,
it will be excluded with match = FALSE.
## [1] 476nbrs_sickle_trait2 <- nbrs_sickle_trait %>%
find_nodes(
pattern = "^Mice",
semtypes = c("humn", "popg", "plnt",
"fish", "food", "edac", "dora", "aggp"),
names = c("Polymerase Chain Reaction", "Mus"),
match = FALSE
)
length(nbrs_sickle_trait2)## [1] 386It is natural to consider a chaining like below as a strategy to iteratively explore outward from a seed idea.
Be careful when implementing this strategy because the
grow_nodes() step has the potential to return far more
nodes than is manageable very quickly. Often after just two sequential
uses of grow_nodes(), the number of nodes returned can be
too large to efficiently sift through unless you conduct substantial
filtering with find_nodes() between uses of
grow_nodes().
Summary
In summary, the rsemmed package provides tools for finding and connecting biomedical concepts.
- The key function for finding (and pruning) concepts (graph nodes) is
the
find_nodes()function. - The key function for finding connections between concepts is
find_paths().- The
make_edge_weights()function will allow you to tailor path-finding by creating custom weights. It requires metadata provided byget_edge_features(). - The
get_middle_nodes(),summarize_semtypes(), andsummarize_predicates()functions all help explore paths/node collections to inform reweighting.
- The
- The key function for finding directly related concepts is
grow_nodes().
Your workflow will likely involve iteration between all of these different components.
Session Info
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rsemmed_1.17.0 igraph_2.1.2 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.6.5 cli_3.6.3 knitr_1.49
## [4] rlang_1.1.4 xfun_0.49 stringi_1.8.4
## [7] generics_0.1.3 jsonlite_1.8.9 glue_1.8.0
## [10] buildtools_1.0.0 htmltools_0.5.8.1 maketools_1.3.1
## [13] sys_3.4.3 sass_0.4.9 rmarkdown_2.29
## [16] tibble_3.2.1 evaluate_1.0.1 jquerylib_0.1.4
## [19] fastmap_1.2.0 yaml_2.3.10 lifecycle_1.0.4
## [22] stringr_1.5.1 BiocManager_1.30.25 compiler_4.4.2
## [25] dplyr_1.1.4 pkgconfig_2.0.3 digest_0.6.37
## [28] R6_2.5.1 utf8_1.2.4 tidyselect_1.2.1
## [31] pillar_1.10.0 magrittr_2.0.3 bslib_0.8.0
## [34] withr_3.0.2 tools_4.4.2 cachem_1.1.0References
Kilicoglu, Halil, Graciela Rosemblat, Marcelo Fiszman, and Thomas C
Rindflesch. 2011. “Constructing a Semantic Predication Gold
Standard from the Biomedical Literature.” BMC
Bioinformatics 12 (December): 486. https://doi.org/10.1186/1471-2105-12-486.