ArticleBMC bioinformatics2019
Analyzing a co-occurrence gene-interaction network to identify disease-gene association.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The interplay of specialized metabolites, antibiotic resistance, and virulence in Enterococcus faecium: in silico analysis of bacterial genomes.BMC genomics · 2026Article
- ENQUIRE automatically reconstructs, expands, and drives enrichment analysis of gene and Mesh co-occurrence networks from context-specific biomedical literature.PLoS computational biology · 2025Article
- Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach.Genes to cells : devoted to molecular & cellular mechanisms · 2024Article
- Unveiling the whole genomic features and potential probiotic characteristics of novelFrontiers in microbiology · 2024Article
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.Bioinformatics (Oxford, England) · 2022Article
- Determination of deleterious single-nucleotide polymorphisms of human LYZ C gene: an in silico study.Journal, genetic engineering & biotechnology · 2022Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens.Genome research · 2022Article
- Brain areas involved with obsessive-compulsive disorder present different DNA methylation modulation.BMC genomic data · 2021Article
- Mining Proteome Research Reports: A Bird's Eye View.Proteomes · 2021Article
- Article
- Systems Genetics for Mechanistic Discovery in Heart Diseases.Circulation research · 2020Review
- Integrative Network Analysis of Predicted miRNA-Targets Regulating Expression of Immune Response Genes in Bovine Coronavirus Infection.Frontiers in genetics · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundUnderstanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes.
resultsWe evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes.
conclusionsThe results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods.
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