What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Machine learning
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 most cited work include:
- Drug-target interaction prediction by random walk on the heterogeneous network. (301 citations)
- Long non-coding RNAs and complex diseases: from experimental results to computational models (296 citations)
- Novel human lncRNA-disease association inference based on lncRNA expression profiles (292 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Computational biology (44.63%)
- Cross-validation (37.19%)
- Artificial intelligence (30.58%)
What were the highlights of his more recent work (between 2018-2021)?
- Cross-validation (37.19%)
- Computational model (26.45%)
- Computational biology (44.63%)
In recent papers he was focusing on the following fields of study:
Xing Chen focuses on Cross-validation, Computational model, Computational biology, Similarity and Artificial intelligence. His Cross-validation research also works with subjects such as
- Inference and related Data mining,
- Data set together with Random forest and Dependency. His studies in Computational model integrate themes in fields like Biological system and Bayesian probability.
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.
Between 2018 and 2021, his most popular works were:
- MicroRNAs and complex diseases: from experimental results to computational models. (214 citations)
- Computational models for lncRNA function prediction and functional similarity calculation. (54 citations)
- LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities. (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- Machine learning
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.
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