Omics Data Mining Methods for Precision Medicine
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 51
Special Issue Editor
Interests: molecular characterization of complex diseases; biological network analysis; identification of molecular regulatory relationships; molecular feature recognition; omics data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Precision medicine aims to tailor treatments to individual characteristics, conditions, and preferences. The differences in the genetic, molecular, and clinical phenotypes of individuals can be reflected through massive amounts of multi-omics data with a high throughput. Traditional biological experiments cannot meet the needs of large-scale knowledge mining, and the huge volume of biological data makes it difficult for biologists to use. Therefore, there is an urgent need to develop computational methods to mine knowledge from multi-omics data to understand biological processes ranging from mutations to gene expression to protein differences to different disease subtypes in order to enable personalized medicine.
Deep learning has supported the research and development of cancer early screening kits, the improvement of treatment technology for major diseases, and prognosis management. We invite the submission of original research articles, systematic reviews, and domain-specific studies that present the latest breakthroughs, challenges, and future directions related to deep learning algorithms within both the basic and clinical domains. We welcome submissions from researchers working at the intersection of artificial intelligence, machine learning, and their practical applications. This Special Issue seeks to foster interdisciplinary collaboration and promote the development of innovative, ethical, and domain-specific solutions that will positively impact computer science, biology, and precision medicine. Submitted papers should adhere to rigorous scientific standards, including clear problem formulation, a repeatable methodology, thorough evaluation, and meaningful interpretation of results within the context of the existing literature and research practice.
The topics covered by this Special Issue include (but are not limited to) the following:
- Novel algorithms for multi-omics integration;
- Machine learning and AI applications in omics data analysis;
- Case studies demonstrating successful omics-driven precision medicine approaches;
- Comparative analyses of existing data mining techniques;
- Challenges in handling high-dimensional omics data;
- Ethical considerations in omics data mining for personalized health;
- Tools and software for data mining in omics research;
- The role of data mining in biomarker discovery;
- Predictive modeling for treatment response based on omics data;
Prof. Dr. Tianyi Zhao
Guest Editor
Manuscript Submission Information
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Keywords
- omics data
- precision medicine
- deep learning
- disease subtype
- biomarker
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