A Factorization Machine Based Deep Neural Network for Synergism of Cancer Drug Combinations Prediction
Xin Ma, Wenbin Lin, Xiaoli Wu, Huifang Xie, Yi Chen, Shengwen Dong, Xiaona Cao · 2021
The advantages of combination therapy for cancer are low toxicity and high efficiency. However, the money and time required to perform drug combination experiments are costly. Therefore, artificial intelligence is born for helping find potential synergistic drug combinations. In this work, a factorization machine based deep neural network (DNN-FM) was proposed for drug combination synergy prediction. The DNN-FM combines the advantage of factorization machines for learning feature interaction and deep neural network for learning complicate feature representation in a novel network architecture. Compared with the traditional machine learning methods, DNN-FM had better performance in processing complicated data. The result of the comprehensive experiments illustratedthat DNN-FM had excellent performance on the efficiency and high accuracy for drug combination synergy prediction.