Package: ttgsea 1.13.0
Dongmin Jung
ttgsea: Tokenizing Text of Gene Set Enrichment Analysis
Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens.
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ttgsea.pdf |ttgsea.html✨
ttgsea/json (API)
NEWS
| # Install 'ttgsea' in R: |
| install.packages('ttgsea', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
On BioConductor:ttgsea-1.13.0(bioc 3.20)ttgsea-1.12.0(bioc 3.19)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:23e12ac089
Exports:bi_grubi_lstmfit_modelmetric_pearson_correlationplot_modelpredict_modelsampling_generatortext_tokentoken_vector
Dependencies:backportsbase64encBHbitbit64bslibcachemclicliprcolorspaceconfigcpp11crayondata.tableDiagrammeRdigestdplyrdttenglishevaluatefansifarverfastmapfastmatchfloatfontawesomefsgenericsglueherehighrhmshtmltoolshtmlwidgetshunspelligraphISOcodesjquerylibjsonlitekerasknitrkoRpuskoRpus.lang.enlabelinglatticelexiconlgrlifecyclemagrittrMatrixMatrixExtramemoisemgsubmimemlapimunsellNLPpillarpkgconfigpngprettyunitsprocessxprogresspspurrrqdapRegexquantedaR6rappdirsRColorBrewerRcppRcppArmadilloRcppTOMLreadrreticulateRhpcBLASctlrlangrmarkdownrprojrootrsparserstudioapisassscalesslamSnowballCstopwordsstringistringrsyllysylly.ensyuzhettensorflowtext2vectextcleantextshapetextstemtfautographtfrunstibbletidyrtidyselecttinytextmtokenizerstzdbutf8vctrsviridisLitevisNetworkvroomwhiskerwithrxfunxml2yamlzeallotzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bidirectional GRU with embedding layer | bi_gru |
| Bidirectional LSTM with embedding layer | bi_lstm |
| Deep learning model fitting | fit_model |
| Pearson correlation coefficient | metric_pearson_correlation |
| visualization of the model architecture | plot_model |
| Model prediction | predict_model |
| Generator function | sampling_generator |
| Tokenizing text | text_token |
| Vectorization of tokens | token_vector |
