A Multi-View Learning-Based Rule Extraction Algorithm For Accurate Hepatotoxicity Prediction

Yuting Zhong, Bowei Yan, KunHong Liu, Yong Xu, Song He, Xiaochen Bo

Abstract: Hepatotoxicity prediction is key to diseases with the high mortality rate. However, most of the algorithms used by now are black box in nature and lack of clear interpretability. This paper proposes a genetic algorithm-based interpretable algorithm based on rules extracted from a random forest. To take advantages from different types of omics data and molecular representations gathered from various datasets, our algorithm utilizes multiple distinct features to form a multi-view learning strategy. In detail, the genetic algorithm is designed to select optimal rules from each view, which are then used to form the ensemble of multi-view rule sets. The experiments are carried out to verify the performance of our algorithm on the hepatotoxicity data. The results confirm that our algorithm can gain high accuracy in most cases with more compact and shorter rules, compared with the original random forest or other rule-based algorithms. Our python source code and the related Supplementary Materials are available at: github.com/MLDMXM2017/MVR-GA.