Your browser doesn't support javascript.
loading
biotextgraph: graphical summarization of functional similarities from textual information.
Sato, Noriaki; Zhang, Yao-Zhong; Gu, Zuguang; Imoto, Seiya.
  • Sato N; Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
  • Zhang YZ; Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
  • Gu Z; Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany.
  • Imoto S; Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Bioinformatics ; 40(6)2024 Jun 03.
Article en En | MEDLINE | ID: mdl-38851878
ABSTRACT

SUMMARY:

Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. AVAILABILITY AND IMPLEMENTATION The package, documentation, and web server are available at https//github.com/noriakis/biotextgraph.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Biología Computacional Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Biología Computacional Idioma: En Año: 2024 Tipo del documento: Article