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Yang Li
Department of Computational Medicine and Bioinformatics
University of Michigan
100 Washtenaw Ave
Ann Arbor, MI 48109

Email: liyangum@umich.edu

About Me

  • 2017-current, visiting student, University of Michigan
  • 2014-2020, Ph.D student, Nanjing University of Science and Technology
  • Research Interests

  • Pattern Recognition
  • Bioinformatics
  • Machine Learning
  • Publications

  • Li, Y., Zhang, C., Bell, E. W., Yu, D. J., & Zhang, Y. (2019). Ensembling multiple raw coevolutionary features with deep residual neural networks for contact‐map prediction in CASP13. Proteins: Structure, Function, and Bioinformatics.

  • Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y. (2019). Deep‐learning contact‐map guided protein structure prediction in CASP13. Proteins: Structure, Function, and Bioinformatics..

  • Zheng, W., Zhang, C., Wuyun, Q., Pearce, R., Li, Y., & Zhang, Y. (2019). LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins. Nucleic acids research..

  • Li, Y., Hu, J., Zhang, C., Yu, D. J., & Zhang, Y. (2019). ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks. Bioinformatics (Oxford, England).

  • Hu, J., Li, Y., Zhang, Y., & Yu, D. J. (2018). ATPbind: accurate protein–ATP binding site prediction by combining sequence-profiling and structure-based comparisons. Journal of chemical information and modeling, 58(2), 501-510.

  • Hu, J., Li, Y., Zhang, M., Yang, X., Shen, H. B., & Yu, D. J. (2017). Predicting protein-DNA binding residues by weightedly combining sequence-based features and boosting multiple SVMs. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 14(6), 1389-1398.

  • Hu, J., Han, K., Li, Y., Yang, J. Y., Shen, H. B., & Yu, D. J. (2016). TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM. Amino Acids, 48(11), 2533.

  • Hu, J., Li, Y., Yan, W. X., Yang, J. Y., Shen, H. B., & Yu, D. J. (2016). KNN-based dynamic query-driven sample rescaling strategy for class imbalance learning. Neurocomputing, 191, 363-373.

  • Hu, J., Li, Y., Yang, J. Y., Shen, H. B., & Yu, D. J. (2016). GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure. Computational biology and chemistry, 60, 59-71.

  • Yu, D. J., Li, Y., Hu, J., Yang, X., Yang, J. Y., & Shen, H. B. (2014). Disulfide connectivity prediction based on modelled protein 3D structural information and random forest regression. IEEE/ACM transactions on computational biology and bioinformatics, 12(3), 611-621.
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