Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 18, Issue 6, Pages 2514-2525Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.2986387
Keywords
Feature extraction; Diseases; Databases; Bioinformatics; Machine learning; Genomics; Molecular biomarker; machine learning; precision medicine; disease diagnosis; gene prioritization
Categories
Funding
- National Natural Science Foundation of China [61932008, 61772368, 11925103]
- National Key R&D Program of China [2018YFC0910500, 2018YF C0116600]
- Natural Science Foundation of Shanghai [17ZR144 5600]
- Shanghai Municipal Science and Technology Major Project [2018SHZDZX01]
- Fundamental Research Funds for young teacher of Guangxi [2017KY0264]
- ZJLab
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Molecular biomarkers are specific molecules that can aid in diagnosing or predicting diseases. Advances in high-throughput technologies have allowed for the accumulation of large omics data for screening biomarkers. Research involves various machine learning approaches to identify biomarkers and address potential challenges in biomedical data for future biomarker identification.
Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.
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