D-Index & Metrics Best Publications
Research.com 2022 Rising Star of Science Award Badge

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Biology and Biochemistry D-index 55 Citations 10,842 120 World Ranking 10452 National Ranking 266
Computer Science D-index 53 Citations 10,497 118 World Ranking 3210 National Ranking 315

Research.com Recognitions

Awards & Achievements

2022 - Research.com Rising Star of Science Award

Overview

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.

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.

Best Publications

Drug-target interaction prediction by random walk on the heterogeneous network.

Xing Chen;Ming-Xi Liu;Gui-Ying Yan.
Molecular BioSystems (2012)

456 Citations

Novel human lncRNA-disease association inference based on lncRNA expression profiles

Xing Chen;Gui-Ying Yan.
Bioinformatics (2013)

451 Citations

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)

429 Citations

Drug–target interaction prediction: databases, web servers and computational models

Xing Chen;Chenggang Clarence Yan;Xiaotian Zhang;Xu Zhang.
Briefings in Bioinformatics (2016)

422 Citations

MicroRNAs and complex diseases: from experimental results to computational models.

Xing Chen;Di Xie;Qi Zhao;Zhu-Hong You.
Briefings in Bioinformatics (2021)

386 Citations

RWRMDA: predicting novel human microRNA–disease associations

Xing Chen;Ming-Xi Liu;Gui-Ying Yan.
Molecular BioSystems (2012)

363 Citations

Semi-supervised learning for potential human microRNA-disease associations inference

Xing Chen;Gui-Ying Yan.
Scientific Reports (2015)

318 Citations

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)

318 Citations

Predicting miRNA-disease association based on inductive matrix completion.

Xing Chen;Lei Wang;Jia Qu;Na-Na Guan.
Bioinformatics (2018)

313 Citations

WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.

Xing Chen;Chenggang Clarence Yan;Xu Zhang;Zhu Hong You.
Scientific Reports (2016)

302 Citations

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