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HisCoM-PAGE: software for hierarchical structural component models for pathway analysis of gene expression data.

Lydia Mok1, Taesung Park1,2

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.

Genomics & Informatics
|January 4, 2020
PubMed
Summary

We developed Hierarchical Structural Component Model for Pathway Analysis of Gene Expression Data (HisCoM-PAGE) software to analyze gene expression data for survival phenotypes. This tool simplifies pathway analysis for researchers studying survival times.

Keywords:
gene expressionhierarchical component modelpathway analysissurvival phenotype

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying biological pathways linked to survival phenotypes is crucial for understanding disease progression.
  • Gene expression data offers a powerful resource for such investigations.
  • Existing methods may not fully leverage the complex relationships between genes and pathways.

Purpose of the Study:

  • To introduce the Hierarchical Structural Component Model for Pathway Analysis of Gene Expression Data (HisCoM-PAGE) software.
  • To provide researchers with an accessible tool for pathway analysis of survival data.
  • To enable the simultaneous analysis of multiple pathways considering their hierarchical structures.

Main Methods:

  • Development of the HisCoM-PAGE software.
  • Incorporation of hierarchical gene and pathway structures into the analysis model.
  • Application to diverse gene expression datasets, including microarray and RNA sequencing data.

Main Results:

  • The HisCoM-PAGE software facilitates pathway analysis for survival phenotypes.
  • The method accounts for hierarchical relationships within biological pathways.
  • It supports the simultaneous analysis of multiple pathways.

Conclusions:

  • HisCoM-PAGE software enhances the accessibility of advanced pathway analysis for survival data.
  • This tool is applicable to various gene expression data types.
  • It is expected to aid researchers in identifying survival-associated pathways more effectively.