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 The University of California, Irvine (UCI or UC Irvine) is a Public university, public Land-grant university, land-grant research university in Irvine, California. One of the ten campuses of the University of California system, UCI offers 87 und ...

in 2006, his MS degree from Utah State University in 2001, and his BS degree from Huazhong University of Science and Technology in 1994. His research interests include

bioinformatics Bioinformatics () is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combi ...

, machine learning and artificial intelligence. His current research is focused on protein structure and function prediction, 3D genome structure modeling, biological network construction, and

deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. De ...

with applications to

big data Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe Big data is the one associated with large body of information that we could not comprehend when used only in smaller am ...

in biomedical domains. Dr. Cheng has more than 180 publications in the field of

computational biology Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has fo ...

, artificial intelligence, and machine learning, which have been cited thousands of times according t
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 o
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

#Chen, C., Chen, X., Morehead, A., Wu, T., Cheng, J. (2023) 3D-equivariant graph neural networks for protein model quality assessment. Bioinformatics, accepted

#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

#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

#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

#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

#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

#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

References

External links

{{DEFAULTSORT:Cheng, Jianlin Jack American computer scientists Living people American people of Chinese descent Year of birth missing (living people)