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. • 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. • 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. • J. Eickholt, J. Cheng. Predicting Protein Residue-Residue Contacts Using Deep Networks and Boosting. Bioinformatics. 28(23):3066-3072, 2012. ==References==