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KMID : 0369820170470040317
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Jorunal of Korean Pharmaceutical Sciences
2017 Volume.47 No. 4 p.317 ~ p.323
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Deep learning: from chemoinformatics to precision medicine
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Kim In-Wha
Oh Jung-Mi
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Abstract
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Deep learning is a new machine learning paradigm that focuses on learning with deep hierarchical models of data. Chemoinformatics has been defined as the mixing of chemical information resources to transform into knowledge for the intended purpose of making better and faster decisions in the area of drug lead identification and optimization. Precision medicine includes disease prevention and treatment strategies that consider individual variability in healthcare. Researchers are now focusing on the convergence of genomics, epigenomics, metabolomics, informatics, and imaging, along with other technologies such as data mining, deep learning, and big data methodology; disciplines that are rapidly expanding the scope of precision medicine. Drug and diagnostic developers, physicians, health systems, and patients share interests in precision medicine. In this review, we provide an overview of recent studies on the application of the deep learning method in the pharmaceuticals and precision medicine fields. We briefly review the fields related to the history of deep learning, chemoinformatics, drug development, model based medicine, electronic healthcare records, wearable sensors, drug response variability, and precision medicine.
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KEYWORD
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Deep learning, Drug discovery and development, Chemoinformatics, Precision medicine
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