DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Do Heon | - |
dc.contributor.advisor | 이도헌 | - |
dc.contributor.author | Jeong, Chungsun | - |
dc.date.accessioned | 2021-05-11T19:31:53Z | - |
dc.date.available | 2021-05-11T19:31:53Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=875241&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/282960 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2019.8,[iv, 51 p. :] | - |
dc.description.abstract | Identification of drug-target protein interactions (DTIs) is important in drug discovery fields. Diverse computational methods have been proposed to find out DTIs efficiently. The dataset generally used in training has only a small number of DTIs, so that many drug-target pairs have no interaction label. Most studies did not consider the mentioned characteristics of the DTI dataset and usually did not utilize the unlabeled data well. In this paper, we predict DTIs by using the features of drugs and targets. In the proposed prediction method, two main models proceed in sequence. The first model is the autoencoder model with multiple hidden layers. By only using the features of arbitrary drug-target pairs as the inputs, the autoencoder is trained without any label information in an unsupervised learning manner. The latent features from the autoencoder are used as the inputs of the second model. In the subsequent model, we train a deep neural network (DNN) classifier with the proposed semi-supervised learning method. Both unlabeled data and labeled data are utilized for training the classifier. The DNN classifier trained by the proposed unified model shows the best performance in all evaluation metrics compared to other supervised models utilizing only labeled data in training. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | drug-target interaction▼adeep neural network▼alatent feature▼asemi-supervised learning▼aunlabeled data | - |
dc.subject | 약물 타겟 간 상호작용▼a심층 신경망▼a잠재 특징▼a준지도 학습▼a미분류 데이터 | - |
dc.title | Predicting drug-target interactions with deep neural networks in semi-supervised learning manner | - |
dc.title.alternative | 준지도 학습 방식의 심층 신경망을 이용한 약물-타겟 단백질의 상호작용 예측 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | 정충순 | - |
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