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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국약제학회 Journal of Pharmaceutical Investigation Journal of Pharmaceutical Investigation 제47권 제4호
- 발행연도
- 2017.7
- 수록면
- 317 - 323 (7page)
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초록· 키워드
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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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참고문헌 (72)
참고문헌 신청
Aliper A / 2016 / Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data / Mol Pharm 13:2524~2530
Aoyama T / 1990 / Neural networks applied to quantitative structure–activity relationship analysis / J Med Chem 33:2583~2590
Baba H / 2015 / Modeling and prediction of solvent effect on human skin permeability using support vector regression and random forest / Pharm Res 32:3604~3617
Banaee H / 2013 / Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges / Sensors 13:17472~17500
Brown FK / 1998 / Chemoinformatics: what is it and how does it impact drug discovery / Annu Rep Med Chem 33:375~384
Aoyama T / 1990 / Neural networks applied to quantitative structure–activity relationship analysis / J Med Chem 33:2583~2590
Baba H / 2015 / Modeling and prediction of solvent effect on human skin permeability using support vector regression and random forest / Pharm Res 32:3604~3617
Banaee H / 2013 / Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges / Sensors 13:17472~17500
Brown FK / 1998 / Chemoinformatics: what is it and how does it impact drug discovery / Annu Rep Med Chem 33:375~384
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