Leveraging Machine Learning for Omics-driven Biomarker Discovery

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Background

Accumulation of omics data at different biological levels has provided an unprecedented opportunity for revolutionized biomarker discovery. Accordingly, multi-omics data have greatly facilitated the systematic research of biological events and processes. Meanwhile, reasonable integration and effective processing of large-scale biomedical datasets of various multimodalities is a path of both promise and challenge. However, the current integrated multi-omics data analysis is facing the following challenges: (1) multi-omics data is generated from different levels, which presents large heterogeneity, and there is currently a lack of efficient data integration methods. (2) Although machine learning techniques have made great progress in recent years, their huge requirements for computational resources have limited the progress of multi-omics research. (3) Lack data explorative tools that incorporate both useful summary statistics and new visualization tools to analyze, integrate, interpret such data. (4) It remains a challenge for researchers and physicians to make the most of multi-omics data and to use their findings to guide clinical practice.

Based on the above challenges, we issued this Research Topic with urgent needs in this cutting-edge field, which involves: 1) bioinformatics and biostatistics pipelines to analyze, integrate, and interpret multi-omics data for efficient biomarker screening, and 2) robust clinical prediction models or markers based on multi-omics data for prognosis assessment and risk stratification of patients.

In this Research Topic, high-quality and innovative submissions relevant to health and diseases in multi-omics (genomic, epigenomic, transcriptomic, metagenomic, metabolomic, and proteomic) data are highly recommended for biomarker discovery and translation. Specific themes include, but are not limited to:

• Methods, applications, databases, and web servers on machine learning (ML) / artificial intelligence (AI) for screening and prioritization of biomarkers or based on large-scale biomedical data, particularly multi-omics.
• Statistical and mathematical modeling for multi-omics data integration and mining for biomarker discovery.
• Methods for assessment of molecular pathway activation and drug action on individual patients, based on multi-omics big data.
• Omics-based personalized medicine for diseases.
• Identification, validation, and annotation of prediction signatures and biomarkers for patient risk stratification and prognostication based on large-scale biomedical data, particularly multi-omics.
• Pre-clinical and clinical studies addressing the biological mechanisms or clinical performance of certain data-driven biomarkers.
• Review of public databases of multi-omics big data, their content, as well as methods and software for its fast browsing and processing
• Artificial intelligence (AI) and its implementation in social, psychological, and public health science.

Please note: manuscripts consisting solely of bioinformatics or computational analysis databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.

Keywords: omics, biomarker, machine learning, big data, translation

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.

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