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 March 2025 | Viewed by 75
Special Issue Editors
Interests: machine learning; computational biomedicine; bioinformatics; physical modelling of biological systems
Special Issues, Collections and Topics in MDPI journals
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
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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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