W
Wenjian Wu
Researcher at Wuhan University
Publications - 5
Citations - 369
Wenjian Wu is an academic researcher from Wuhan University. The author has contributed to research in topics: Ensemble learning & Semantic similarity. The author has an hindex of 5, co-authored 5 publications receiving 239 citations.
Papers
More filters
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
SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions
TL;DR: The sequence-based feature projection ensemble learning method, “SFPEL-LPI”, is proposed, which accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods.
Journal ArticleDOI
Feature-derived graph regularized matrix factorization for predicting drug side effects
TL;DR: A novel computational method “feature-derived graph regularized matrix factorization” (FGRMF), which predicts unobserved side effects for approved drugs based on known drug-side effect associations and available drug features and outperforms benchmark side effect prediction methods on the benchmark datasets.
Journal ArticleDOI
Sequence-based bacterial small RNAs prediction using ensemble learning strategies.
TL;DR: WAEM and NNEM can produce better results than existing state-of-the-art sRNA prediction methods and are helpful for understanding the biological mechanism of bacteria.
Proceedings ArticleDOI
Predicting small RNAs in bacteria via sequence learning ensemble method
TL;DR: The sequence learning ensemble method, which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs, and the genetic algorithm is adopted to optimize the parameters in the ensemble system, outperforms existing state-of-the-art sRNA prediction methods.