Machine Learning and AI Approaches for Genomic and Transcriptomic Insights in Health and Disease
A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".
Deadline for manuscript submissions: 30 November 2026
Special Issue Editor
Interests: machine learning and deep learning modelling of biological complexities; large language models; bioinformatics; high volume biological data-driven modelling and analysis.
Special Issue Information
Dear Colleagues,
This Special Issue presents a collection of cutting-edge research that harnesses the power of machine learning, deep learning, and advanced bioinformatics to unravel the intricate complexities of biological systems. In the era of big data, high-throughput omics technologies have generated massive, heterogeneous datasets that demand sophisticated analytical frameworks. The development of scalable, AI-driven methodologies for data integration and interpretation has become essential to decode the molecular mechanisms underlying complex diseases such as cancer, diabetes, and Alzheimer’s. Of particular interest is the emergence of DNA language models (DLMs) and computational grammars that apply principles from natural language processing to genomic sequences, enabling the discovery of latent patterns, contextual dependencies, and regulatory syntax embedded within the genome. By leveraging large language models and deep neural architectures, these approaches offer novel insights into genotype–phenotype relationships, biomarker identification, and a systems-level understanding of disease processes. The articles featured in this Special Issue underscore the transformative potential of data-driven, language-inspired frameworks in biomedical research, paving the way for more precise, predictive, and personalized approaches in diagnostics and therapeutics.
Dr. Sushil Shakyawar
Guest Editor
Manuscript Submission Information
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Keywords
- AI applications in biology and medicine
- deep learning approaches in multiomic data integration and analysis
- DNA/protein language modeling
- computational genomics
- AI-driven disease modeling
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