【目的】旨在發現潛在的抗腫瘤藥物作用靶點,為日后臨床工作及實驗驗證提供參考。【方法】從DrugBank數據庫獲取抗腫瘤藥物靶點,結合HPRD數據庫中蛋白質相互作用信息,使用Cytoscape建立藥物靶點PPI網絡并計算網絡節點的拓撲屬性,使用SPSS單因素分析和Weka信息增益原理篩選拓撲屬性變量,采用SMOTE算法處理不平衡數據集問題,利用決策樹方法構建抗腫瘤藥物靶點預測模型,并與其他三種常見的機器學習分類算法模型進行性能比較。【結果】應用決策樹算法構建的抗腫瘤藥物靶點預測模型的預測準確率達73.18%,在CBioPortal中驗證發現,結果中預測分數大于等于0.9的16個靶點在多種腫瘤中存在突變和擴增,并以NR5A1為例進行具體分析。【局限】僅使用抗腫瘤藥物靶點的PPI網絡屬性構建預測模型,未加入靶點的功能、序列屬性等特征。【結論】基于PPI網絡的拓撲屬性,采用機器學習方法對潛在的抗腫瘤藥物靶點進行預測是有效的,可以為抗腫瘤藥物的研發及臨床工作提供一定參考。

[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.