AI/Machine Learning-Driven Multi-Omics Research in Oncology
A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 30
Special Issue Editors
Interests: tumor immune microenvironment; tumor immuno-therapy; tumor biology; bioinformatics; application of biomaterials in medical field; AI
2. School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
Interests: tumor marker; metabolism; evidence-based Medicine
Interests: Immune; tumor marker; artificial intelligence
Special Issue Information
Dear Colleagues,
The integration of high-throughput biotechnologies and advanced computational biology is fundamentally reshaping oncology research. This Special Issue, entitled "AI/Machine Learning-Driven Multi-Omics Research in Oncology," is dedicated to exploring the transformative potential at the nexus of these disciplines. It seeks to showcase pioneering studies that harness artificial intelligence and machine learning to decode the complex, multi-layered biology of cancer.
As a systems-level disease, cancer arises from intricate interactions across genomic, transcriptomic, proteomic, epigenomic, and metabolomic dimensions. While multi-omics profiling offers an unprecedented, holistic perspective on tumor biology, the volume, heterogeneity, and complexity of the resulting data pose significant analytical challenges. Conventional statistical approaches are often inadequate for detecting the subtle, non-linear patterns and interactions inherent in such datasets.
This is where AI and machine learning emerge as essential. This Special Issue will highlight research employing advanced computational methods to integrate and interpret multi-omics data. We welcome contributions that not only introduce innovative algorithms but also yield meaningful biological insights and clinical applications. Key topics include the identification of novel biomarkers and therapeutic targets, refinement of molecular subtyping, elucidation of drug resistance mechanisms, and progress in personalized treatment strategies.
By compiling groundbreaking work in this rapidly evolving domain, the Special Issue aims to catalyze the next wave of advances in precision oncology. We invite submissions that illustrate how AI-powered multi-omics analysis can lead to a deeper understanding of cancer biology and, ultimately, to improved patient outcomes.
Dr. Wenjun Mao
Dr. Renjun Gu
Dr. Yize Mao
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- multi-omics
- oncology
- precision medicine
- biomarker discovery
- cancer genomics
- deep learning
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