Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.8
Journals
Genes
Special Issues
Machine Learning in Cancer and Disease Genomics
share announcement

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 1617

Special Issue Editors

Prof. Dr. Piero Fariselli

E-Mail Website
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
Dr. Giovanni Birolo

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

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
Viewed by 341
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)
►▼ Show Figures

Figure 1

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 644
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

34 pages, 9334 KiB  
Review
A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research
by Flavio Sartori, Francesco Codicè, Isabella Caranzano, Cesare Rollo, Giovanni Birolo, Piero Fariselli and Corrado Pancotti
Genes 2025, 16(6), 648; https://doi.org/10.3390/genes16060648 - 28 May 2025
Abstract
The integration of deep learning (DL) with multi-omics data has significantly advanced our understanding of biological systems, particularly in cancer research. DL enables the analysis of high-dimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and [...] Read more.
The integration of deep learning (DL) with multi-omics data has significantly advanced our understanding of biological systems, particularly in cancer research. DL enables the analysis of high-dimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and management. This review provides a detailed overview of recent developments in deep learning models applied to genomics data, with a focus on cancer type classification, driver gene identification, survival analysis, and drug response prediction. We introduce the foundational concepts of machine and deep learning and explain the characteristics of multi-omics data, addressing a broad and interdisciplinary audience. Methods published since 2020 are systematically reviewed, including their model architectures, datasets, and key innovations. Full article
(This article belongs to the Special Issue Machine Learning in Cancer and Disease Genomics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop