Dr. Cheng has more than 180 publications in the field of bioinformatics, computational biology, artificial intelligence, and machine learning, which have been cited thousands of times according to Google Scholar Citations. He and his students developed one of the first deep learning methods for protein structure prediction and demonstrated that deep learning was the best method for protein structure prediction for the first time in the 10th community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP10) in 2012. His protein structure prediction methods (MULTICOM) supported by the National Institutes of Health (NIH) and the National Science Foundation (NSF) were consistently ranked among the top methods during the last several rounds of the community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP) from 2008 to 2022. Dr. Cheng was a recipient of 2012 NSF CAREER award for his work on 3D genome structure modeling. He is a fellow of American Institute for Medical and Biological Engineering (AIMBE) and a fellow of Asia-Pacific Artificial Intelligence Association (AAIA).
Selected publicationsedit
Chen, C., Chen, X., Morehead, A., Wu, T., Cheng, J. (2023) 3D-equivariant graph neural networks for protein model quality assessment. Bioinformatics, accepted. [1]
Guo, Z., Liu, J., Skolnick, J., Cheng, J. (2022) Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks. Nature Communications. 13:6963. [2].
Liu, J., Wu, T., Guo, Z., Hou, J., & Cheng, J. (2022). Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14. Proteins: Structure, Function, and Bioinformatics, 90(1), 58-72. [3]
Chen, C., Wu, T., Guo, Z., & Cheng, J. (2021). Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction. Proteins: Structure, Function, and Bioinformatics, 89(6), 697-707. [4]
Wu, T., Guo, Z., Hou, J., & Cheng, J. (2021). DeepDist: real-value inter-residue distance prediction with deep residual convolutional network. BMC bioinformatics, 22, 1-17. [5]
Hou, J., Wu, T., Cao, R., & Cheng, J. (2019). Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins: Structure, Function, and Bioinformatics, 87(12), 1165-1178. [6]
T. Trieu, J. Cheng. Large-scale reconstruction of 3D structures of human chromosomes from chromosomal contact data. Nucleic Acids Research. 42(7):e52, 2014. paper
M. Zhu, J. Dahmen, G. Stacey, J. Cheng. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data. BMC Bioinformatics. 14:278, 2013. paper
J. Eickholt, J. Cheng. A Study and Extension of DNcon: a Method for Protein Residue-Residue Contact Prediction Using Deep Networks. BMC Bioinformatics. 14(Suppl 14):S12, 2013. paper
J. Eickholt, J. Cheng. Predicting Protein Residue-Residue Contacts Using Deep Networks and Boosting. Bioinformatics. 28(23):3066-3072, 2012. paper
Dr. Cheng's Bioinformatics and Machine Learning Laboratory (BML) homepage.
January 01, 1970
jianlin, cheng, this, article, autobiography, been, extensively, edited, subject, someone, connected, subject, need, editing, conform, wikipedia, neutral, point, view, policy, there, relevant, discussion, talk, page, 2023, learn, when, remove, this, message, j. This article is an autobiography or has been extensively edited by the subject or by someone connected to the subject It may need editing to conform to Wikipedia s neutral point of view policy There may be relevant discussion on the talk page May 2023 Learn how and when to remove this message Jianlin Jack Cheng is the William and Nancy Thompson Missouri Distinguished Professor in the Electrical Engineering and Computer Science EECS Department at the University of Missouri Columbia He earned his PhD from the University of California Irvine in 2006 his MS degree from Utah State University in 2001 and his BS degree from Huazhong University of Science and Technology in 1994 1 His research interests include bioinformatics machine learning and artificial intelligence His current research is focused on protein structure and function prediction 2 3D genome structure modeling 3 biological network construction 4 and deep learning with applications to big data in biomedical domains Dr Cheng has more than 180 publications in the field of bioinformatics computational biology artificial intelligence and machine learning which have been cited thousands of times according to Google Scholar Citations He and his students developed one of the first deep learning methods for protein structure prediction and demonstrated that deep learning was the best method for protein structure prediction for the first time in the 10th community wide Critical Assessment of Techniques for Protein Structure Prediction CASP10 in 2012 His protein structure prediction methods MULTICOM supported by the National Institutes of Health NIH and the National Science Foundation NSF were consistently ranked among the top methods during the last several rounds of the community wide Critical Assessment of Techniques for Protein Structure Prediction CASP from 2008 to 2022 Dr Cheng was a recipient of 2012 NSF CAREER award for his work on 3D genome structure modeling He is a fellow of American Institute for Medical and Biological Engineering AIMBE and a fellow of Asia Pacific Artificial Intelligence Association AAIA Selected publications editChen C Chen X Morehead A Wu T Cheng J 2023 3D equivariant graph neural networks for protein model quality assessment Bioinformatics accepted 1 Guo Z Liu J Skolnick J Cheng J 2022 Prediction of inter chain distance maps of protein complexes with 2D attention based deep neural networks Nature Communications 13 6963 2 Liu J Wu T Guo Z Hou J amp Cheng J 2022 Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14 Proteins Structure Function and Bioinformatics 90 1 58 72 3 Chen C Wu T Guo Z amp Cheng J 2021 Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction Proteins Structure Function and Bioinformatics 89 6 697 707 4 Wu T Guo Z Hou J amp Cheng J 2021 DeepDist real value inter residue distance prediction with deep residual convolutional network BMC bioinformatics 22 1 17 5 Hou J Wu T Cao R amp Cheng J 2019 Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13 Proteins Structure Function and Bioinformatics 87 12 1165 1178 6 T Trieu J Cheng Large scale reconstruction of 3D structures of human chromosomes from chromosomal contact data Nucleic Acids Research 42 7 e52 2014 paper M Zhu J Dahmen G Stacey J Cheng Predicting gene regulatory networks of soybean nodulation from RNA Seq transcriptome data BMC Bioinformatics 14 278 2013 paper J Eickholt J Cheng A Study and Extension of DNcon a Method for Protein Residue Residue Contact Prediction Using Deep Networks BMC Bioinformatics 14 Suppl 14 S12 2013 paper J Eickholt J Cheng Predicting Protein Residue Residue Contacts Using Deep Networks and Boosting Bioinformatics 28 23 3066 3072 2012 paperReferences edit Cheng Jianlin Mizzou Engineering The MULTICOM Toolbox for Protein Structure Prediction NSF CAREER Project Analysis Construction Visualization and Modeling of 3D Genome Structures MU Center for Botanical Interaction Studies External links editDr Cheng s Bioinformatics and Machine Learning Laboratory BML homepage Retrieved from https en wikipedia org w index php title Jianlin Cheng amp oldid 1155722239, wikipedia, wiki, book, books, library,
