TY - JOUR
T1 - Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine
AU - Grapov, Dmitry
AU - Fahrmann, Johannes
AU - Wanichthanarak, Kwanjeera
AU - Khoomrung, Sakda
N1 - Publisher Copyright:
© 2018 Dmitry Grapov, et al. Published by Mary Ann Liebert, Inc.
PY - 2018/10
Y1 - 2018/10
N2 - Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.
AB - Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.
KW - Artificial intelligence
KW - Biomarkers
KW - Deep learning
KW - Machine learning
KW - Multiomics data integration
KW - Precision medicine
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U2 - 10.1089/omi.2018.0097
DO - 10.1089/omi.2018.0097
M3 - Article
C2 - 30124358
AN - SCOPUS:85055203338
SN - 1536-2310
VL - 22
SP - 630
EP - 636
JO - OMICS A Journal of Integrative Biology
JF - OMICS A Journal of Integrative Biology
IS - 10
ER -