2022 - Research.com Rising Star of Science Award
Xing Chen mainly investigates Cross-validation, Computational biology, Semantic similarity, Computational model and Bioinformatics. His study looks at the relationship between Cross-validation and topics such as Inference, which overlap with Regularized least squares and Semi-supervised learning. In his study, which falls under the umbrella issue of Computational biology, Machine learning is strongly linked to Similarity.
His Semantic similarity research focuses on subjects like Similarity, which are linked to Predictive modelling. His Computational model study combines topics in areas such as Biological network, Drug discovery and Identification. His Bioinformatics research incorporates themes from microRNA, Data science and Disease Association.
His primary areas of investigation include Computational biology, Cross-validation, Artificial intelligence, Computational model and Similarity. His Computational biology research incorporates elements of microRNA, Semantic similarity, Kernel and Small molecule. His biological study spans a wide range of topics, including Data mining, Identification, Inference, Regularized least squares and Disease Association.
Xing Chen interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence. As part of the same scientific family, Xing Chen usually focuses on Computational model, concentrating on Drug discovery and intersecting with Database, DrugBank and Drug. He has researched Similarity in several fields, including Recommender system and Receiver operating characteristic.
Xing Chen focuses on Cross-validation, Computational model, Computational biology, Similarity and Artificial intelligence. His Cross-validation research also works with subjects such as
His work carried out in the field of Computational biology brings together such families of science as microRNA, Semantic similarity, Kernel and Function. The study incorporates disciplines such as Receiver operating characteristic, Algorithm and Identification in addition to Similarity. He has included themes like Optimization problem, Machine learning, Drug discovery and Pattern recognition in his Artificial intelligence study.
Xing Chen spends much of his time researching Computational model, Computational biology, Machine learning, Cross-validation and Artificial intelligence. The concepts of his Computational model study are interwoven with issues in RNA, Methylation, Sequencing data and Database. Xing Chen integrates several fields in his works, including Computational biology and Identification.
His Similarity research extends to Machine learning, which is thematically connected. His Cross-validation study incorporates themes from Stability, Classifier, Decision tree learning, Feature vector and Optimization problem. His research integrates issues of Feature descriptor, Logistic model tree, Semantic similarity and Sequence analysis in his study of microRNA.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Drug-target interaction prediction by random walk on the heterogeneous network.
Xing Chen;Ming-Xi Liu;Gui-Ying Yan.
Molecular BioSystems (2012)
Novel human lncRNA-disease association inference based on lncRNA expression profiles
Xing Chen;Gui-Ying Yan.
Bioinformatics (2013)
Long non-coding RNAs and complex diseases: from experimental results to computational models
Xing Chen;Chenggang Clarence Yan;Xu Zhang;Zhu-Hong You.
Briefings in Bioinformatics (2016)
Drug–target interaction prediction: databases, web servers and computational models
Xing Chen;Chenggang Clarence Yan;Xiaotian Zhang;Xu Zhang.
Briefings in Bioinformatics (2016)
MicroRNAs and complex diseases: from experimental results to computational models.
Xing Chen;Di Xie;Qi Zhao;Zhu-Hong You.
Briefings in Bioinformatics (2021)
RWRMDA: predicting novel human microRNA–disease associations
Xing Chen;Ming-Xi Liu;Gui-Ying Yan.
Molecular BioSystems (2012)
Semi-supervised learning for potential human microRNA-disease associations inference
Xing Chen;Gui-Ying Yan.
Scientific Reports (2015)
PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
Zhu-Hong You;Zhi-An Huang;Zexuan Zhu;Gui-Ying Yan.
PLOS Computational Biology (2017)
Predicting miRNA-disease association based on inductive matrix completion.
Xing Chen;Lei Wang;Jia Qu;Na-Na Guan.
Bioinformatics (2018)
WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.
Xing Chen;Chenggang Clarence Yan;Xu Zhang;Zhu Hong You.
Scientific Reports (2016)
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