Application of Machine Learning in Genetic Diseases
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".
Deadline for manuscript submissions: 10 May 2025 | Viewed by 70
Special Issue Editors
Interests: time series analysis; forecasting; artificial intelligence; biomedical engineering; complex problems
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; statistical analysis in big data; machine learning algorithms; data mining; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning algorithms; data mining; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rise in next-generation sequencing (NGS) technologies, along with their lowering cost, has led to the production of a huge number of diverse and complex data in biology and medicine that must be efficiently managed. Traditional approaches and classical analysis methods are struggling to provide efficient and time-sensitive solutions. Machine learning and deep learning have become an invaluable tool with which large genomic datasets can be analyzed to decode the complexities of molecular and biological systems. Machine learning is now broadly applied across various genetic fields, including cancer genetics, epigenetics, transcriptomics (single-cell, differential expression, etc.), functional genomics, and pharmacogenetics.
Therefore, this Special Issue will serve to promote relevant advances in the application of machine learning and deep learning in genetics. We encourage authors to contribute work exploring any area of machine learning being applied to genetic data (not only limited to the fields listed here). We welcome submissions of original research, review articles, brief reports, or other related forms. We also invite authors who have made relevant contributions to the recent International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2024) to extend their conference publication through the addition of new results and to submit it to this Special Issue.
Prof. Dr. Ignacio Rojas
Prof. Dr. Olga Valenzuela
Dr. Francisco Ortuño
Guest Editors
Manuscript Submission Information
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Keywords
- next-generation sequencing
- machine learning
- deep learning
- genetic data
- epigenetics
- transcriptomics
- functional genomics
- pharmacogenetics
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