Development of machine learning-based methods for predicting herb-drug interactions = 허브-약물 상호작용 예측을 위한 기계학습 기반 방법론 개발

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Recently, attention on herb-drug interactions has increased due to the extensive popularity of herbal products or dietary supplements. So far, herb-drug interactions mostly have been investigated through in-vivo/in-vitro experiments. However, the number of herb-drug interactions to test is dramatically increasing, and therefore the need for in-silico methods is rising to reduce the search space and costs. Here, we develop two machine-learning-based methods to investigate synergistic herb-drug interactions. In the first study, we develop a method based on the deep neural network to predict the Caco-2 monolayer permeability of chemical compounds, such as herbal compounds. We used all possible molecular descriptors as the input feature of our model and compared the overall performance with recently developed methods, which only used several molecular descriptors. We show that our approach has better prediction performance compared to previous studies. In the second study, we define new disease-specific features to predict synergistic compound combinations. We show that the newly defined disease-specific features allow us to investigate the synergism of various combinations of compounds, which could not be considered in previous studies. Lastly, by combining the two methods, we suggest novel herb-drug pairs that may express synergistic effect.
Advisors
Lee, Kwang Hyungresearcher이광형researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2018.2,[iv, 90 p. :]

Keywords

Machine-learning▼aherb-drug interaction▼adeep neural network▼afeature extraction▼anetwork analysis; 기계학습▼a허브-약물 상호작용▼a심층신경망▼a특징추출▼a네트워크 분석

URI
http://hdl.handle.net/10203/264697
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=867891&flag=dissertation
Appears in Collection
BiS-Theses_Ph.D.(박사논문)
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