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Machine Learning and Integrative Analysis of Biomedical Big Data

by Bilal Mirza 1,2,*, Wei Wang 1,3,4,5, Jie Wang 1,2, Howard Choi 1,2,5, Neo Christopher Chung 1,6 and Peipei Ping 1,2,4,5,7,*
1
NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA
2
Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA
3
Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA
4
Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA
5
Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA 90095, USA
6
Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland
7
Department of Medicine (Cardiology), University of California Los Angeles, Los Angeles, CA 90095, USA
*
Authors to whom correspondence should be addressed.
Genes 2019, 10(2), 87; https://doi.org/10.3390/genes10020087
Received: 2 December 2018 / Revised: 8 January 2019 / Accepted: 21 January 2019 / Published: 28 January 2019
(This article belongs to the Special Issue Systems Analytics and Integration of Big Omics Data)
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. View Full-Text
Keywords: machine learning; multi-omics; data integration; curse of dimensionality; heterogeneous data; missing data; class imbalance; scalability machine learning; multi-omics; data integration; curse of dimensionality; heterogeneous data; missing data; class imbalance; scalability
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Mirza, B.; Wang, W.; Wang, J.; Choi, H.; Chung, N.C.; Ping, P. Machine Learning and Integrative Analysis of Biomedical Big Data. Genes 2019, 10, 87.

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