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Xue-wen Chen
Researcher at Wayne State University
Publications - 108
Citations - 5851
Xue-wen Chen is an academic researcher from Wayne State University. The author has contributed to research in topics: Feature selection & Deep learning. The author has an hindex of 34, co-authored 108 publications receiving 5121 citations. Previous affiliations of Xue-wen Chen include University of Kansas & University of Science and Technology of China.
Papers
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Journal ArticleDOI
Big Data Deep Learning: Challenges and Perspectives
Xue-wen Chen,Xiaotong Lin +1 more
TL;DR: A brief overview of deep learning is provided, and current research efforts and the challenges to big data, as well as the future trends are highlighted.
Journal ArticleDOI
Machine learning and its applications to biology.
TL;DR: This tutorial discusses the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data in the field of supervised learning in R, the open source data analysis and visualization language.
Journal ArticleDOI
Prediction of protein--protein interactions using random decision forest framework
Xue-wen Chen,Mei Liu +1 more
TL;DR: This paper introduces a domain-based random forest of decision trees to infer protein interactions that is capable of exploring all possible domain interactions and making predictions based on all the protein domains.
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Combating the Small Sample Class Imbalance Problem Using Feature Selection
M Wasikowski,Xue-wen Chen +1 more
TL;DR: This paper presents a first systematic comparison of the three types of methods developed for imbalanced data classification problems and of seven feature selection metrics evaluated on small sample data sets from different applications and showed that signal-to-noise correlation coefficient and Feature Assessment by Sliding Thresholds are great candidates for feature selection in most applications.
Journal ArticleDOI
Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs
Mei Liu,Yonghui Wu,Yukun Chen,Jingchun Sun,Zhongming Zhao,Xue-wen Chen,Michael E. Matheny,Hua Xu +7 more
TL;DR: A machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information is proposed.