Package: ttgsea Type: Package Title: Tokenizing Text of Gene Set Enrichment Analysis Description: 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. Version: 1.21.0 Date: 2021-11-12 Authors@R: c(person(given="Dongmin", family="Jung", email="dmdmjung@gmail.com", role=c("cre", "aut"), comment = c(ORCID = "0000-0001-7499-8422"))) Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown SystemRequirement: tensorflow License: Artistic-2.0 biocViews: Software, GeneExpression, GeneSetEnrichment NeedsCompilation: no VignetteBuilder: knitr Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libpng-dev libuv1-dev libxml2-dev python3 libx11-dev Repository: https://bioc.r-universe.dev Date/Publication: 2026-04-28 12:54:38 UTC RemoteUrl: https://github.com/bioc/ttgsea RemoteRef: HEAD RemoteSha: ff8c567427e943e39c372c45bb598b4252bbf54c Packaged: 2026-06-16 07:43:21 UTC; root Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung