Computational Cancer Biology: Artificial Intelligence and Machine Learning for Multi-Omics Interrogation: Identification of Biomarkers, Disease-Related Processes and Drug Targets
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 15 January 2027 | Viewed by 23
Special Issue Editor
Interests: data engineering; drug target discovery; building agentic solutions for the interrogation of biomedical data; artificial intelligence incorporation into process engineering; machine learning; precision medicine; deep learning; bioinformatics; systems biology; process modelling and mining; molecular driver identification; drug target identification from data; understanding molecular processes in drug re-purposing; insilico prototyping of diagnostics; data mining of complex data sets
Special Issue Information
Dear Colleagues,
This Special Issue aims to present advances and studies on the transformative role of artificial intelligence and machine learning in cancer biology to accelerate cancer research and improve patient outcomes.
Cancer research has entered an era of unprecedented data generation through genomics, transcriptomics, proteomics, metabolomics, and epigenomics. However, extracting meaningful biological insights from these complex, high-dimensional datasets is challenging and requires sophisticated computational approaches. Harmonisation of multidisciplinary approaches in this space, and integrating computational methods and high-dimensional omics data, offers the opportunity to expedite discovery.
We invite original research articles and comprehensive reviews that address AI/ML methodologies for cancer multi-omics integration, novel biomarker discovery for diagnosis and prognosis, identification of disease-driving mechanisms and pathways, and computational drug target identification and therapeutic strategy development. Topics of interest include deep learning architectures for omics analysis, network-based approaches to cancer biology, predictive modeling for treatment response, and interpretable AI methods that provide biological insights.
This Special Issue welcomes reviews as well as original research articles, which should be submitted by 15 January 2027.
Prof. Dr. Graham Ball
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-blind 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
- artificial intelligence
- machine learning
- multi-omics integration
- biomarker discovery
- precision oncology
- drug target identification
- computational cancer biology
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