Gelsius: a literature-based workflow for determining quantitative associations between genes and biological processes

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput experiments generate vast biomedical data.
• Analyzing gene-biological process relationships is crucial but challenging.
• Existing tools lack methods for quantifying these correlations from literature.

Purpose of the Study:

• To develop a methodology for extracting and quantifying gene-biological process correlations from biomedical literature.
• To introduce Gelsius, a novel workflow for this purpose.
• To facilitate the analysis of high-throughput experimental results.

Main Methods:

• Developed a workflow incorporating biomedical literature.
• Implemented modules for query expansion and document canonicalization.
• Utilized latent semantic analysis for correlation measurement.

Main Results:

• Gelsius quantifies gene-biological process correlations.
• Demonstrated effectiveness on known gene-function relationships.
• Identified novel candidate relationships for experimental validation.

Conclusions:

• Gelsius is the first tool to measure gene-biological process correlation from literature.
• The workflow enhances the analysis of genomic data.
• Enables discovery of new biological insights and experimental hypotheses.