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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
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MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization.
Lingtao Su1,2, Guixia Liu3,4, Tian Bai5,6
1College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
BMC Bioinformatics
|June 7, 2018
Summary
This study introduces a new gene prioritization method, MGOGP, which considers gene modules and Gene Ontology (GO) information. MGOGP effectively identifies more cancer-related genes than existing methods, aiding disease understanding and treatment.
Area of Science:
- Genomics
- Computational Biology
- Cancer Research
Background:
- Gene prioritization is crucial for cancer research.
- Current methods often overlook gene module information.
- Dysfunctional gene modules are linked to cancer development.
Purpose of the Study:
- To propose a novel gene prioritization method, MGOGP (Module and Gene Ontology-based Gene Prioritization).
- To incorporate both individual gene and gene module information for improved cancer-related gene prioritization.
- To leverage Gene Ontology (GO) and known cancer genes as heuristics.
Main Methods:
- Developed MGOGP, a method considering individual genes and their affiliated modules.
- Utilized Gene Ontology (GO) based fuzzy measure values.
- Incorporated known cancer-related genes as heuristics for prioritization.
Main Results:
- MGOGP was validated using breast and prostate cancer datasets.
- The method was compared against existing gene prioritization approaches.
- MGOGP demonstrated superior performance, prioritizing more literature-confirmed cancer-related genes.
Conclusions:
- This work enhances understanding of the genetic basis of complex diseases.
- The findings can improve cancer diagnosis accuracy.
- The method has potential to increase the effectiveness of cancer therapies.
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