Deep Learning for Bioinformatics and Biomedicine
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 September 2026 | Viewed by 186
Special Issue Editor
Interests: molecular bioinformatics; deep learning in biomedicine; multi-omics data integration; computational pathology; explainable artificial intelligence (XAI); biomarker discovery; radiogenomics; precision medicine
Special Issue Information
Dear Colleagues,
Bioinformatics has become a cornerstone of modern biomedical research, driven by the rapid expansion of high-throughput technologies such as genomics (DNA), transcriptomics, proteomics, metabolomics, and spatial omics. In parallel, advances in histopathology and medical imaging enable detailed characterization of tissue architecture and cellular phenotypes. Together, these multimodal data sources provide complementary insights into the molecular and structural basis of human diseases.
Despite these advances, the integration and interpretation of large-scale, heterogeneous biological data remain major challenges. Recent developments in deep learning and artificial intelligence have significantly transformed the analysis of complex biomedical datasets, enabling the identification of disease-associated molecular patterns, biomarker discovery, and the modeling of biological systems across multiple scales. These approaches also facilitate the linkage between genomic alterations and phenotypic manifestations observed in histopathological and clinical data.
This Special Issue aims to provide a platform for cutting-edge research on the development and application of deep learning methods in bioinformatics and biomedicine, with a strong emphasis on molecular mechanisms. Topics of interest include, but are not limited to: identification of disease biomarkers from genome, transcriptome, proteome, and metabolome data; discovery of drug targets and drug response prediction; analysis of clinical and imaging data; histopathological image analysis; radiogenomics; molecular network modeling; epigenetic marker discovery; and early disease screening using artificial intelligence.
We particularly encourage studies that enhance biological interpretability, reproducibility, and translational impact. Contributions integrating multi-omics data with imaging or histopathology, as well as those supporting precision medicine and systems-level understanding of disease, are especially welcome.
Dr. Burak Tasci
Guest Editor
Manuscript Submission Information
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Keywords
- bioinformatics
- deep learning
- genomics (DNA)
- histopathology
- multi-omics
- molecular biomarkers
- drug discovery
- computational pathology
- systems biology
- precision medicine
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