Machine Learning and Integrative Analysis of Biomedical Big Data

Bilal Mirza; Wei Wang; Jie Wang; Howard Choi; Neo Christopher Chung; Peipei Ping

doi:10.3390/genes10020087

journal article Open Access Jan 28, 2019

Machine Learning and Integrative Analysis of Biomedical Big Data

Bilal Mirza Wei Wang

Jie Wang

Howard Choi

Neo Christopher Chung

Peipei Ping

Genes Vol. 10 No. 2 pp. 87 · MDPI AG

View at Publisher Save 10.3390/genes10020087

Abstract

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

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Details

Published: Jan 28, 2019
Vol/Issue: 10(2)
Pages: 87
License: View

Authors

B

Bilal Mirza

NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA

W

Wei Wang

NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA; Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA 90095, USA

J

Jie Wang

NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA

H

Howard Choi

NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA 90095, USA

N

Neo Christopher Chung

NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

P

Peipei Ping

NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA; Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine (Cardiology), University of California Los Angeles, Los Angeles, CA 90095, USA

Funding

National Institutes of Health Award: R35-HL135772

Cite This Article

Bilal Mirza, Wei Wang, Jie Wang, et al. (2019). Machine Learning and Integrative Analysis of Biomedical Big Data. Genes, 10(2), 87. https://doi.org/10.3390/genes10020087

Machine Learning and Integrative Analysis of Biomedical Big Data

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