This research topic aims to explore the advancements and applications of machine learning (ML), especially deep learning (DL), methods in addressing the challenges posed by the vast and complex datasets in computational genomics. The primary objectives include investigating how AI can enhance our understanding of genomic structures, gene-environment interactions, gene regulatory mechanisms, and genetic disorders. Specific questions to be addressed include how AI can improve the analysis of genomic structures, facilitate multi-omics data integration, and contribute to the discovery of novel therapeutic targets. The research will also test hypotheses related to the effectiveness of AI in modeling complex biological networks and its potential to revolutionize personalized medicine.
To gather further insights into the application of AI in bioinformatics, data involved can be at any one or more levels, such as bulk, single cell, or spatial levels. We welcome articles addressing, but not limited to, the following themes:
• Calling and differential analysis of genomic structures such as compartments, TADs, or loops using Hi-C data.
• Analysis of genomic structures and functions using various sequencing technologies (e.g., DNA-seq, RNA-seq, Hi-C, ATAC-seq, ChIP-seq).
• Prediction of RNA secondary and tertiary structures to understand RNA-RNA and RNA-protein interactions.
• Analysis of spatial transcriptomics, epigenetics, and proteomics data.
• Integration and interpretation of multi-omics data and its applications.
• Analysis of biomedical data, including imaging, electronic medical records, and clinical data.
• Development of biomedical regulatory networks for molecular mechanism annotation.
• Prediction of ncRNA-disease interactions to discover novel therapeutic targets.
• Integrative analysis of omics and biomedical data for biomarker discovery and disease prognostics, diagnostics, and treatment.
• Prospects of future advancements in AI for bioinformatics.
Keywords: High-throughput sequencing (HTS), Omics data, Genome insights, Gene regulation, Compartment analysis (Hi-C data), RNA-RNA/RNA-protein relationship
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
