Bioinformatics and Machine Learning for Predicting Biological Processes
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 30 May 2025 | Viewed by 1370
Special Issue Editor
Interests: focused on developing machine learning/deep learning tools for identifying DNA, RNA, and protein modification sites, with current interest in developing computational pipelines to generate embeddings and identify cell types from single-cell Hi-C data
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Special Issue Information
Dear Colleagues,
The rapid advancement of bioinformatics and machine learning has revolutionized the way in which we study and predict complex biological processes. As the volume of biological data continues to grow, novel computational approaches are increasingly required to interpret high-dimensional datasets and discover the mechanisms that underlie biological phenomena. From understanding gene regulation and protein interactions to elucidating disease pathways, bioinformatics tools, combined with cutting-edge machine learning algorithms, offer unprecedented opportunities to accelerate progress in the life sciences. This Special Issue seeks to explore the intersection of these fields, providing insights into how innovative computational methods can address various challenges associated with biology and medicine.
The aim of this Special Issue is to provide a platform for researchers to share advancements in bioinformatics and machine learning methodologies specifically designed to predict biological processes. We welcome original research articles, reviews, and application-focused studies that utilize computational tools to model, predict, and interpret key biological events, such as gene expression, epigenetic modifications, metabolic pathways, and disease progression. Submissions that incorporate novel algorithms, explainable machine learning models, or integrative multi-omics approaches are particularly welcome. By highlighting innovative research, this Special Issue seeks to foster interdisciplinary collaboration and drive innovation at the forefront of bioinformatics and machine learning.
Dr. Lv Hao
Guest Editor
Manuscript Submission Information
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Keywords
- bioinformatics
- machine learning
- multi-omics integration
- gene regulation
- biological processes
- disease processes
- data-driven discovery
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