Interpretable AI and Multiomics Integration for Cancer Treatment Response Prediction: Cutting-Edge Innovations and Overcoming Challenges
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 25 October 2026 | Viewed by 311
Editor
Interests: AI for cancer genomics; foundation models for DNA and enhancer prediction; multi-omics integration and epigenetics; early detection and tumour subtype classification; explainable and energy-efficient machine learning
Special Issue Information
Dear Colleagues,
Recent advances in artificial intelligence, including deep learning, genomic language models, and transformer-based architectures, are redefining how we understand tumour biology and predict patient outcomes. Although progress has been significant, challenges remain in achieving reliable generalisation across cohorts, improving interpretability, and developing clinically practical, energy-efficient solutions. This Special Issue aims to present high-quality research that addresses these challenges and strengthens the connection between computational innovation and real clinical impact. We welcome contributions involving enhancer-focused regulatory modelling, multiomics data integration, DNA and RNA foundation models, and clinical prediction pipelines designed to support patient stratification, therapy selection, and early detection of relapse.
The purpose of this Special Issue is to present new insights into how modern machine learning, including explainable machine learning and interpretable foundation models, can improve the accuracy, transparency, and clinical relevance of cancer treatment response prediction.
This Special Issue welcomes reviews as well as original research articles, which should be submitted by 25 October 2026.
Dr. Gholamreza Rafiee
Guest Editor
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 communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly 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 2900 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
- genomic language models
- multiomics integration
- enhancer prediction
- interpretable AI
- tumour stratification
- deep learning
- translational oncology
- cancer treatment response
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