Machine Learning in Cancer and Disease Genomics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 1172

Special Issue Editors


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Guest Editor
Department of Medical Sciences, University of Torino, 10126 Torino, Italy
Interests: machine learning; computational biomedicine; bioinformatics; physical modelling of biological systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Medical Sciences, University of Torino, 10126 Torino, Italy
Interests: machine learning; computational biomedicine; bioinformatics; genomics

Special Issue Information

Dear Colleagues,

This Special Issue, "Machine Learning in Cancer and Disease Genomics," aims to highlight the pivotal role that advanced computational techniques play in the understanding and treatment of complex diseases. Machine learning algorithms have shown great potential in identifying patterns and making predictions from vast and complex genomic data, thus contributing significantly to personalized medicine and targeted therapies. Machine learning (also known as artificial intelligence) has revolutionized various fields by providing powerful tools to analyze large datasets and uncover hidden patterns. In the context of genomics, these techniques are crucial for making sense of the massive amounts of data encoded in the human genome.

This Special Issue seeks to cover a wide range of topics, including, but not limited to, the development of new machine learning methods for the analysis of genomic data, focusing on applications to cancer and other complex diseases, the integration of multi-omics data, machine learning for biomarker discovery, genome interpretation, and prediction of the effects of genomic variation on disease phenotypes or on DNA/RNA and proteins. We welcome submissions that present cutting-edge research, novel methodologies, and comprehensive reviews in the field. We encourage contributions exploring innovative ways to leverage machine learning in genomics, developing tools for genomic data integration and analysis, and variant impacts on cancer and complex diseases.

Prof. Dr. Piero Fariselli
Dr. Giovanni Birolo
Guest Editors

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Keywords

  • machine learning
  • cancer genomics
  • disease genomics
  • bioinformatics
  • genomic data analysis
  • biomarker discovery
  • multi-omics integration
  • personalized medicine
  • computational biology

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Published Papers (2 papers)

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Research

21 pages, 10131 KiB  
Article
Development and Experimental Validation of Machine Learning-Based Disulfidptosis-Related Ferroptosis Biomarkers in Inflammatory Bowel Disease
by Yongchao Liu, Jing Shao, Jie Zhang, Mengmeng Sang, Qiuyun Xu and Liming Mao
Genes 2025, 16(5), 496; https://doi.org/10.3390/genes16050496 - 27 Apr 2025
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Abstract
Background: Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract, defined by intestinal epithelial cell death. While ferroptosis and disulfidptosis have been linked to IBD pathogenesis, the functional significance of disulfidptosis-related ferroptosis genes (DRFGs) in this disease remains poorly [...] Read more.
Background: Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract, defined by intestinal epithelial cell death. While ferroptosis and disulfidptosis have been linked to IBD pathogenesis, the functional significance of disulfidptosis-related ferroptosis genes (DRFGs) in this disease remains poorly characterized. This investigation sought to pinpoint DRFGs as diagnostic indicators and clarify their mechanistic contributions to IBD progression. Methods: Four IBD datasets (GSE65114, GSE87473, GSE102133, and GSE186582) from the GEO database were integrated to identify differentially expressed genes (DEGs) (|log2FC| > 0.585, adj. p < 0.05). A Pearson correlation analysis was used to link disulfidptosis and ferroptosis genes, followed by machine learning (LASSO and RF) to screen core DRFGs. The immune subtypes and single-cell sequencing (GSE217695) results were analyzed. A DSS-induced colitis Mus musculus (C57BL/6) model was used for validation. Results: Transcriptomic profiling identified 521 DEGs, with 16 defined as DRFGs. Nine hub genes showed diagnostic potential (AUC: 0.71–0.91). Functional annotation demonstrated that IBD-associated genes regulate diverse pathways, with a network analysis revealing their functional synergy. The PPI networks prioritized DUOX2, NCF2, ACSL4, GPX2, CBS, and LPCAT3 as central hubs. Two immune subtypes exhibited divergent DRFG expression. Single-cell mapping revealed epithelial/immune compartment specificity. The DSS-induced murine colitis model confirmed differential expression patterns of DRFGs, with concordant results between qRT-PCR and RNA-seq, emphasizing their pivotal regulatory roles in disease progression and potential for translational application. Conclusions: DRFGs mediate IBD progression via multi-signal pathway regulation across intestinal cell types, demonstrating diagnostic and prognostic potential. Full article
(This article belongs to the Special Issue Machine Learning in Cancer and Disease Genomics)
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23 pages, 4971 KiB  
Article
Common Regulatory Mechanisms Mediated by Cuproptosis Genes in Inflammatory Bowel Disease and Major Depressive Disorder
by Jiyuan Shi, Qianyi Wu, Mengmeng Sang and Liming Mao
Genes 2025, 16(3), 339; https://doi.org/10.3390/genes16030339 - 14 Mar 2025
Viewed by 554
Abstract
Background: The prevalence of major depressive disorder (MDD) among patients with inflammatory bowel disease (IBD) is significantly higher compared to the general population, suggesting a potential link between their pathogeneses. Cuproptosis, defined as cell death caused by intracellular copper accumulation, has not been [...] Read more.
Background: The prevalence of major depressive disorder (MDD) among patients with inflammatory bowel disease (IBD) is significantly higher compared to the general population, suggesting a potential link between their pathogeneses. Cuproptosis, defined as cell death caused by intracellular copper accumulation, has not been thoroughly investigated in the context of IBD and MDD. This study aims to uncover the molecular mechanisms of cuproptosis-related genes (CRGs) in both conditions and to explore novel therapeutic strategies by the modulation of CRGs. Methods: In this study, we identified differentially expressed CRGs between normal and disease samples. We calculated the correlation among CRGs and between CRGs and immune cell infiltrations across various tissues. Four machine learning algorithms were employed to identify key CRGs associated with IBD and MDD. Additionally, drug sensitivity, molecular docking, and molecular dynamics simulations were conducted to predict therapeutic drugs for IBD and MDD. Results: We identified DLD, DLAT, DLST, PDHB, and DBT as common DE-CRGs, and DLD, LIAS, SLC31A1, SCO2, and CDKN2A as key CRGs associated with both IBD and MDD. Consequently, DLD was recognized as a shared biomarker in both diseases. A total of 37 potential therapeutic drugs were identified for IBD and MDD. Based on the molecular docking and molecular dynamics simulation analyses, barasertib and NTP-TAE684, which target DLAT, were predicted to be the most effective compounds. Conclusions: These findings have substantially enhanced our understanding of the similarities and differences in the regulatory mechanisms of CRGs within brain–gut axis diseases. Key biomarkers have been identified, and potential therapeutic drugs have been predicted to effectively target IBD and MDD. Full article
(This article belongs to the Special Issue Machine Learning in Cancer and Disease Genomics)
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