A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

• Published in: BMC genomics 2023-02, Vol.24 (1), p.76-76, Article 76
• Main Authors: Zhang, Yan, Shi, Weiyu, Sun, Yeqing
• Format: Article
• Language: English
• Subjects: Algorithms; Biomarkers; Clustering; COVID-19; Datasets; Functional gene module; Gene expression; Gene expression data; Gene Expression Profiling - methods; Gene Ontology; Gene Regulatory Networks; Genes; Genetic algorithm; Genetic algorithms; Genomics; Humans; Identification; Identification methods; Modules; Molecular modelling; Ontology; Optimization; Overlapping gene module; Partitioning around medoids; Principal components analysis; Radiation protection; Similarity; Transcriptome
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-023-09157-z

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.