F
Feng Liu
Researcher at Wuhan University
Publications - 15
Citations - 811
Feng Liu is an academic researcher from Wuhan University. The author has contributed to research in topics: Feature selection & Ensemble learning. The author has an hindex of 9, co-authored 15 publications receiving 522 citations.
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Journal ArticleDOI
Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data
TL;DR: The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction, as the ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods.
Journal ArticleDOI
Predicting drug-disease associations by using similarity constrained matrix factorization
TL;DR: A user-friendly web server is developed by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD, which makes use of known drug-disease associations, drug features and disease semantic information.
Journal ArticleDOI
Predicting drug side effects by multi-label learning and ensemble learning.
TL;DR: A novel method ‘feature selection-based multi-label k-nearest neighbor method’ (FS-MLKNN), which can simultaneously determine critical feature dimensions and construct high-accuracy multi- label prediction models, which are promising tools for the side effect prediction.
Journal ArticleDOI
A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs
TL;DR: Compared with other state-of-the-art methods, the proposed genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNA prediction is promising and can lead to better performances.
Proceedings ArticleDOI
Drug side effect prediction through linear neighborhoods and multiple data source integration
TL;DR: This paper proposes the linear neighborhood similarity method (LNSM), which utilizes single-source data for the side effect prediction, and extends LNSM to deal with multi- source data, and proposes two data integration methods which can effectively integrate multi-sourceData integration methods, which outperform other state-of-the-art side effect Prediction methods in the cross validation and independent test.