ArticleBMC bioinformatics2018
MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- The Cancer Research Database (CRDB): Integrated Platform to Gain Statistical Insight Into the Correlation Between Cancer and COVID-19.JMIR cancer · 2022Article
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
- A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions.Methods (San Diego, Calif.) · 2019Article
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5 authors.
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Abstract
backgroundPrioritizing genes according to their associations with a cancer allows researchers to explore genes in more informed ways. By far, Gene-centric or network-centric gene prioritization methods are predominated. Genes and their protein products carry out cellular processes in the context of functional modules. Dysfunctional gene modules have been previously reported to have associations with cancer. However, gene module information has seldom been considered in cancer-related gene prioritization.
resultsIn this study, we propose a novel method, MGOGP (Module and Gene Ontology-based Gene Prioritization), for cancer-related gene prioritization. Different from other methods, MGOGP ranks genes considering information of both individual genes and their affiliated modules, and utilize Gene Ontology (GO) based fuzzy measure value as well as known cancer-related genes as heuristics. The performance of the proposed method is comprehensively validated by using both breast cancer and prostate cancer datasets, and by comparison with other methods. Results show that MGOGP outperforms other methods, and successfully prioritizes more genes with literature confirmed evidence.
conclusionsThis work will aid researchers in the understanding of the genetic architecture of complex diseases, and improve the accuracy of diagnosis and the effectiveness of therapy.
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