World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
11257
World Ranking
4589
National Ranking
615

Overview

Yuedong Yang is affiliated with Sun Yat-sen University in China and has contributed extensively to the field of Biochemistry, Genetics and Molecular Biology. Their main body of research includes numerous publications with a focus on the subfields of Molecular Biology, Computational Theory and Mathematics, Artificial Intelligence, Materials Chemistry, and Cancer Research.

Their published works cover a range of main research topics, including:

  • Computational Drug Discovery Methods
  • Bioinformatics and Genomic Networks
  • Protein Structure and Dynamics
  • Machine Learning in Bioinformatics
  • Single-cell and spatial transcriptomics
  • Gene expression and cancer classification
  • RNA and protein synthesis mechanisms

Yuedong Yang has coauthored frequently with several researchers, including:

  • Shuangjia Zheng
  • Jiahua Rao
  • Yutong Lu
  • Huiying Zhao

Their work has been published in various research venues, notably:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Briefings in Bioinformatics
  • arXiv (Cornell University)
  • Journal of Chemical Information and Modeling
  • Nature Communications

Selected recent papers include:

  • Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images, 2021, IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images, 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • Predicting drug-protein interaction using quasi-visual question answering system, 2020, Nature Machine Intelligence
  • Structure-aware protein-protein interaction site prediction using deep graph convolutional network, 2021, Bioinformatics
  • Integrating multi-omics data through deep learning for accurate cancer prognosis prediction, 2021, Computers in Biology and Medicine

Best Publications

  • Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.

    Ying Song;Shuangjia Zheng;Liang Li;Xiang Zhang

  • Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

    Rhys Heffernan;Yuedong Yang;Kuldip K. Paliwal;Yaoqi Zhou

  • Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images

    Song Ying;Shuangjia Zheng;Liang Li;Xiang Zhang

  • Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates

    Yuedong Yang;Eshel Faraggi;Huiying Zhao;Yaoqi Zhou

  • Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.

    Rhys Heffernan;Kuldip Paliwal;James Lyons;Abdollah Dehzangi

  • Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

    Jack Hanson;Yuedong Yang;Kuldip K. Paliwal;Yaoqi Zhou

  • Predicting drug–protein interaction using quasi-visual question answering system

    Shuangjia Zheng;Yongjian Li;Sheng Chen;Jun Xu

  • SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

    Eshel Faraggi;Tuo Zhang;Tuo Zhang;Yuedong Yang;Yuedong Yang;Lukasz A. Kurgan;Lukasz A. Kurgan

  • Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

    Yuedong Yang;Jianzhao Gao;Jihua Wang;Rhys Heffernan

  • Specific interactions for ab initio folding of protein terminal regions with secondary structures

    Yuedong Yang;Yaoqi Zhou

  • Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks.

    Shuangjia Zheng;Jiahua Rao;Zhongyue Zhang;Jun Xu;Jun Xu

  • Structural insights into the histone H1-nucleosome complex

    Bing-Rui Zhou;Hanqiao Feng;Hidenori Kato;Liang Dai

  • Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks

    Jack Hanson;Kuldip K. Paliwal;Thomas Litfin;Yuedong Yang

  • JAK2-binding long noncoding RNA promotes breast cancer brain metastasis.

    Shouyu Wang;Ke Liang;Qingsong Hu;Ping Li

  • Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks.

    Jack Hanson;Kuldip K. Paliwal;Thomas Litfin;Yuedong Yang;Yuedong Yang

  • SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks

    Yuedong Yang;Rhys Heffernan;Kuldip Paliwal;James Lyons

  • Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

    James G. Lyons;Abdollah Dehzangi;Abdollah Dehzangi;Rhys Heffernan;Alok Sharma;Alok Sharma

  • Structure-aware protein-protein interaction site prediction using deep graph convolutional network.

    Qianmu Yuan;Jianwen Chen;Huiying Zhao;Yaoqi Zhou

  • Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions

    Yuedong Yang;Yaoqi Zhou;Yaoqi Zhou

  • Integrating multi-omics data through deep learning for accurate cancer prognosis prediction

    Hua Chai;Xiang Zhou;Zhongyue Zhang;Jiahua Rao

  • Community-wide assessment of protein-interface modeling suggests improvements to design methodology

    Sarel J. Fleishman;Sarel J. Fleishman;Timothy A. Whitehead;Eva Maria Strauch;Jacob E. Corn;Jacob E. Corn

  • Predicting Continuous Local Structure and the Effect of Its Substitution for Secondary Structure in Fragment-Free Protein Structure Prediction

    Eshel Faraggi;Yuedong Yang;Shesheng Zhang;Yaoqi Zhou;Yaoqi Zhou

  • PharmKG: a dedicated knowledge graph benchmark for bomedical data mining

    Shuangjia Zheng;Jiahua Rao;Ying Song;Jixian Zhang

  • Communicative Representation Learning on Attributed Molecular Graphs

    Ying Song;Shuangjia Zheng;Zhangming Niu;Zhang-hua Fu;Zhang-hua Fu

Frequent Co-Authors

Yaoqi Zhou
Yaoqi Zhou Griffith University
Kuldip K. Paliwal
Kuldip K. Paliwal Griffith University
David Neil Cooper
David Neil Cooper Cardiff University
Matthew Mort
Matthew Mort Cardiff University
Yunlong Liu
Yunlong Liu Indiana University
Abdollah Dehzangi
Abdollah Dehzangi Rutgers, The State University of New Jersey
Abdul Sattar
Abdul Sattar Griffith University
Alok Sharma
Alok Sharma Griffith University
Alan Wee-Chung Liew
Alan Wee-Chung Liew Griffith University
Ying Xu
Ying Xu University of Georgia

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