D-Index & Metrics Best Publications

D-Index & Metrics

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 44 Citations 6,992 82 World Ranking 13522 National Ranking 329

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)

397 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)

368 Citations

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

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

358 Citations

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

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

325 Citations

RWRMDA: predicting novel human microRNA–disease associations

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

302 Citations

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

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

280 Citations

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

Xing Chen;Di Xie;Qi Zhao;Zhu-Hong You.
Briefings in Bioinformatics (2019)

253 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)

243 Citations

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

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

240 Citations

Predicting miRNA-disease association based on inductive matrix completion.

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

221 Citations

Best Scientists Citing Xing Chen

Zhu-Hong You

Zhu-Hong You

Chinese Academy of Sciences

Publications: 89

Fang-Xiang Wu

Fang-Xiang Wu

University of Saskatchewan

Publications: 31

Jianxin Wang

Jianxin Wang

Central South University

Publications: 30

Quan Zou

Quan Zou

University of Electronic Science and Technology of China

Publications: 29

Qinghua Cui

Qinghua Cui

Peking University

Publications: 18

Jijun Tang

Jijun Tang

University of South Carolina

Publications: 18

Min Li

Min Li

Central South University

Publications: 16

Xiangxiang Zeng

Xiangxiang Zeng

Hunan University

Publications: 16

Dong-Qing Wei

Dong-Qing Wei

Shanghai Jiao Tong University

Publications: 15

Xiaoli Li

Xiaoli Li

Agency for Science, Technology and Research

Publications: 12

De-Shuang Huang

De-Shuang Huang

Tongji University

Publications: 10

Peter Vandenabeele

Peter Vandenabeele

Ghent University

Publications: 8

Hartmut Jaeschke

Hartmut Jaeschke

University of Kansas

Publications: 8

Qingming Huang

Qingming Huang

Chinese Academy of Sciences

Publications: 7

Tao Huang

Tao Huang

Chinese Academy of Sciences

Publications: 7

Bin Liu

Bin Liu

Nanjing University

Publications: 7

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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