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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (12): 98-108    DOI: 10.11925/infotech.2096-3467.2018.0545
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Predicting Antineoplastic Drug Targets Based on Network Properties
Xinyue Fan,Lei Cui()
School of Medical Informatics, China Medical University, Shenyang 110122, China
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[Objective] This paper tries to identify potential targets of antineoplastic drugs, aiming to provide references for future clinical work and experiment. [Methods] First, we retrieved the targets of antineoplastic drugs from the DrugBank database, which were also combined with the protein interaction information from the HPRD database. Then, we established the PPI network for these targets with Cytoscape and calculated the topology properties of the nodes. Third, we used SPSS single factor analysis and Weka’s information gain principle to choose the variables for topological attributes. Fourth, we introduced the SMOTE algorithm to process unbalanced data sets and constructed the prediction model for antineoplastic drug targets with the decision tree method. Finally, we compared the performance of our new model with those of the classic ones. [Results] The precision of the proposed model reached 73.18%. With the help of CBioPortal, we found 16 targets’ prediction scores higher than 0.9. These targets could mutate and amplify in various tumors, which were analyzed with the case of NR5A1. [Limitations] The characteristics of target functions, sequence attributes, and other factors should also be included to construct the model. [Conclusions] The proposed model could predict the potential targets of antineoplastic drugs effectively.

Key wordsPPI Network      Machine Learning      Decision Tree      Antineoplastic Drug Targets Prediction     
Received: 15 May 2018      Published: 16 January 2019

Cite this article:

Xinyue Fan,Lei Cui. Predicting Antineoplastic Drug Targets Based on Network Properties. Data Analysis and Knowledge Discovery, 2018, 2(12): 98-108.

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