ArticleFrontiers in cell and developmental biology2021
Identify Inflammatory Bowel Disease-Related Genes Based on Machine Learning.
Article in Frontiers in cell and developmental biology, 2021. 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, 6 citations in OpenAlex.
- Review
- Molecular Insight into the Role of HLA Genotypes in Immunogenicity and Secondary Refractoriness to Anti-TNF Therapy in IBD Patients.International journal of molecular sciences · 2025Review
- ImmunoMet Oncogenesis: A New Concept to Understand the Molecular Drivers of Cancer.Journal of clinical medicine · 2025Review
- Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach.Journal of inflammation research · 2025Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
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Abstract
The patients of Inflammatory bowel disease (IBD) are increasing worldwide. IBD has the characteristics of recurring and difficult to cure, and it is also one of the high-risk factors for colorectal cancer (CRC). The occurrence of IBD is closely related to genetic factors, which prompted us to identify IBD-related genes. Based on the hypothesis that similar diseases are related to similar genes, we purposed a SVM-based method to identify IBD-related genes by disease similarities and gene interactions. One hundred thirty-five diseases which have similarities with IBD and their related genes were obtained. These genes are considered as the candidates of IBD-related genes. We extracted features of each gene and implemented SVM to identify the probability that it is related to IBD. Ten-cross validation was applied to verify the effectiveness of our method. The AUC is 0.93 and AUPR is 0.97, which are the best among four methods. We prioritized the candidate genes and did case studies on top five genes.
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