Paper Info
Title
Learning important features from multi-view data to predict drug side effects
Abstract
The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot of computational algorithms have been developed to predict side effects with different types of drug information. However, there is still a lack of methods which could integrate heterogeneous data to predict side effects and select important features at the same time. Here, we propose a novel computational framework based on multi-view and multi-label learning for side effect prediction. Four different types of drug features are collected and graph model is constructed from each feature profile. After that, all the single view graphs are combined to regularize the linear regression functions which describe the relationships between drug features and side effect labels. L1 penalties are imposed on the regression coefficient matrices in order to select features relevant to side effects. Additionally, the correlations between side effect labels are also incorporated into the model by graph Laplacian regularization. The experimental results show that the proposed method could not only provide more accurate prediction for side effects but also select drug features related to side effects from heterogeneous data. Some case studies are also supplied to illustrate the utility of our method for prediction of drug side effects.
Year
DOI
Venue
2019
10.1186/s13321-019-0402-3
Journal of Cheminformatics
Keywords
DocType
Volume
Side effect prediction, Heterogeneous data integration, Feature selection
Journal
11
Issue
ISSN
Citations 
1
1758-2946
2
PageRank 
References 
Authors
0.36
0
7
Name
Order
Citations
PageRank
Xujun Liang120.36
Pengfei Zhang2588.34
Jun Li320.36
Ying Fu420.36
Lingzhi Qu520.36
Yongheng Chen6184.50
Zhuchu Chen7270.88