The rapid advancement of high-throughput sequencing technologies and sophisticated computational algorithms is significantly transforming biomedical research. This transformation is evidenced by the large-scale generation of multi-dimensional omics data, including genomics, transcriptomics, proteomics, and metabolomics. While these datasets hold tremendous potential for analytical revelations and tailored clinical interventions, challenges such as accurate analysis, comprehensive validation of biomarkers, and seamless translation of bioinformatics insights into clinical practice remain unresolved. These challenges are particularly pronounced in the context of complex and heterogeneous diseases, including cancer, neurodegenerative, autoimmune, and rare genetic disorders.
This Research Topic focuses on addressing the distinct gap in the integration of multi-omics datasets with cutting-edge artificial intelligence techniques, emphasizing the role of advanced machine learning and deep learning algorithms. Our aim is to deepen the understanding of intricate disease mechanisms, reinforce the robustness and clinical validity of predictive models, and hasten the discovery of actionable biomarkers and novel therapeutic targets. Ultimately, the goal is to significantly forward precision medicine by providing personalized predictive tools and targeted treatments with proven clinical relevance.
We invite original and systematic research studies, technology reports, and methodological articles that delve into these specific themes:
• Comprehensive integration and analysis of multi-omics data to expose in-depth molecular disease mechanisms, especially in the context of cancer, neurological, and autoimmune diseases. • Rigorous advancement and clinical validation of machine learning models, prioritizing aspects of transparency, reproducibility, and generalizability of risk stratification across diverse populations. • Comprehensive bioinformatics endeavors directly aimed at systematic biomarker discovery, characterization, and validation within clinical settings. • AI-driven drug screening environments and predictive computational drug repurposing validated through empirical methodologies. • Application of single-cell RNA-seq, spatial transcriptomics, and metabolomics techniques for holistic disease heterogeneity analysis and biomarker exploration. • Deployment of deep learning and advanced neural networks for genomic applications, generating robust disease-specific genetic predictions. • Systems medicine and network biology aimed at detailing, interpreting, and validating molecular interactions relevant to therapeutic discoveries and drug targeting. • Integration of AI technologies in precision medicine, emphasizing personalized therapeutic decisions in oncology, rare genetic disorders, immune-mediated, and chronic complex diseases.
This Research Topic aspires to catalyze precision medicine advancements through distinct computational innovations and stringent experimental and clinical validations, fostering robust multidisciplinary collaborations among computational scientists, experimental biologists, clinical researchers, and healthcare practitioners to enhance global patient outcomes.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
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.
