Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.
Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.
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DOI：
10.1089/omi.2018.0097
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发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
Khoomrung S
中科院分区：
文献类型：
--
作者：
Grapov D;Fahrmann J;Wanichthanarak K;Khoomrung S
机器学习（ML）正被广泛地应用于日常产品中，如互联网搜索、电子邮件垃圾邮件过滤器、产品推荐、图像分类和语音识别。高度集成的制造和自动化的新方法，如工业4.0和物联网，也正在与ML方法融合。许多方法结合了复杂的人工神经网络架构，统称为深度学习（DL）应用程序。这些方法已被证明能够在许多不同形式的数据中表示和学习可预测的关系，并有望改变组学研究和精准医学应用的未来。组学和电子健康记录数据对DL提出了相当大的挑战。这是由于许多因素，如低信噪比，分析方差和复杂的数据集成要求。然而，DL模型已经被证明能够提高数据编码的容易性和预测模型的性能。在深度学习中遇到的概念与在生物信息传递系统（如基因、蛋白质和代谢物网络）中观察到的概念有相似之处，这可能并不奇怪。这篇专家评论探讨了DL在系统和生物规模上为精准医学读者带来的挑战和机遇。
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.
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影响因子：
18.2
作者：
Gómez-Bombarelli R;Wei JN;Duvenaud D;Hernández-Lobato JM;Sánchez-Lengeling B;Sheberla D;Aguilera-Iparraguirre J;Hirzel TD;Adams RP;Aspuru-Guzik A
通讯作者：
Aspuru-Guzik A
影响因子：
3.7
作者：
Grapov, Dmitry;Adams, Sean H.;Newman, John W.
通讯作者：
Newman, John W.
影响因子：
7.4
作者：
Date, Yasuhiro;Kikuchi, Jun
通讯作者：
Kikuchi, Jun
DOI：
10.1002/wsbm.1198
发表时间：
2013-01
影响因子：
7.9
作者：
Chen, Rui;Snyder, Michael
通讯作者：
Snyder, Michael
DOI：
10.1093/biostatistics/kxv026
发表时间：
2016-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Jacob L;Gagnon-Bartsch JA;Speed TP
通讯作者：
Speed TP