Dataset Information

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs.

ABSTRACT: Determining the target genes that interact with drugs-drug-target interactions-plays an important role in drug discovery. Identification of drug-target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug-target interactions. We constructed a drug-target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug-target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug-target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug-target interactions.

SUBMITTER: Xuan P

PROVIDER: S-EPMC6555260 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs.

Xuan Ping P Sun Chang C Zhang Tiangang T Ye Yilin Y Shen Tonghui T Dong Yihua Y

Frontiers in genetics 20190531

Determining the target genes that interact with drugs-drug-target interactions-plays an important role in drug discovery. Identification of drug-target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, ...[more]

PMID: 31214240

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Project description:Circular RNA (circRNA) is a closed-loop structural non-coding RNA molecule which plays a significant role during the gene regulation processes. There are many previous studies shown that circRNAs can be regarded as the sponges of miRNAs. Thus, circRNA is also a key point for disease diagnosing, treating and inferring. However, traditional experimental approaches to verify the associations between the circRNA and disease are time-consuming and money-consuming. There are few computational models to predict potential circRNA-disease associations, which become our motivation to propose a new computational model. In this study, we propose a machine learning based computational model named Gradient Boosting Decision Tree with multiple biological data to predict circRNA-disease associations (GBDTCDA). The known circRNA-disease associations' data are downloaded from cricR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). The feature vector of each circRNA-disease association pair is composed of four parts, which are the statistics information of different biological networks, the graph theory information of different biological networks, circRNA-disease associations' network information and circRNA nucleotide sequence information, respectively. Therefore, we use those feature vectors to train the gradient boosting decision tree regression model. Then, the leave one out cross validation (LOOCV) is adopted to evaluate the performance of our computational model. As for predicting some common diseases related circRNAs, our method GBDTCDA also obtains the better results. The Area under the ROC Curve (AUC) values of Basal cell carcinoma, Non-small cell lung cancer and cervical cancer are 95.8%, 88.3% and 93.5%, respectively. For further illustrating the performance of GBDTCDA, a case study of breast cancer is also supplemented in this study. Thus, our proposed method GBDTCDA is a powerful tool to predict potential circRNA-disease associations based on experimental results and analyses.

Dataset Information

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs.

Publications

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs.

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