- Ph.D., Computer Science, University of Missouri-Columbia, 2016
- M.S., Computer Science, University of Science and Technology of China, 2011
- B.S., Computer Science, Anhui Normal University, 2008
Areas of Emphasis or Expertise
- Machine learning
- Data science
- 23rd International Conference on Intelligent Systems for Molecular Biology (ISMB), Large-Scale Model Quality Assessment for Improving Protein Tertiary Structure Prediction (2015)
- The 5th International Conference on Wireless Communications Networking and Mobile Computing, WiCOM, Apply Modified Method of Nonlinear Optimization to Improve Localization Accuracy in WSN, Beijing (2009)
- R. Cao, C. Freitas, L. Chan, M. Sun, H. Jiang, Z. Chen. "ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network." Molecules 2017:
- R. Cao, B. Adhikari, D. Bhattacharya, M. Sun, J. Hou, J. Cheng. "QAcon: single model quality assessment using protein structural and contact information with machine learning techniques." Bioinformatics 2016: 694.
- R. Cao, D. Bhattacharya, J. Hou, J. Cheng. "DeepQA: improving the estimation of single protein model quality with deep belief networks." BMC bioinformatics Vol. 17.1, 2016: 495.
- D. Bhattacharya, R. Cao, J. Cheng. "UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling." Bioinformatics 2016:
- D. Bhattacharya, J. Nowotny, R. Cao, J. Cheng. "3Drefine: An Interactive Web Server for Efficient Protein Structure Refinement." Nucleic Acids Research Vol. 10.1093, 2016:
- R. Cao, J. Cheng. "Protein single-model quality assessment by feature-based probability density functions." Scientific Reports 2016:
- J. Nowotny, A. Wells, O. Oluwadare, L. Xu, R. Cao, T. Trieu, C. He, J. Cheng. "GMOL: an interactive tool for 3D genome structure visualization." Scientific Reports 2016:
- J. Nowotny, S. Ahmed, L. Xu, O. Oluwadare, H. Chen, N. Hensley, T. Trieu, R. Cao, J. Cheng. "an chromosomes from chromosomal contact data." BMC bioinformatics Vol. 16(1), 2015: 338.
- R. Cao, J. Cheng. "Deciphering the association between gene function and spatial gene-gene interactions in 3D human genome conformation." BMC Genomics Vol. 16, 2015: 880.
- J. Li,R. Cao, J. Cheng. "A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11." BMC bioinformatics 2015:
- R. Cao, Bhattacharya, B. Adhikari, J. Li, J. Cheng. "Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11Proteins." 2015:
- R. Cao, J. Cheng. "Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks." Methods 2015:
- B. Adhikari, D. Bhattacharya, R. Cao, J. Cheng. "CONFOLD: Residue-Residue Contact-guided ab initio Protein Folding Proteins." Vol. 83(8), 2015: 1436-1439.
- R. Cao, Z. Wang, Y. Wang, J. Cheng. "SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines." BMC Bioinformatics Vol. 15, 2014: 120.
- R. Cao, Z. Wang, J. Cheng. "Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment." BMC Structural Biology Vol. 14, 2014: 13.
- Z. Wang, R. Cao, K. Taylor, A. Briley, C. Caldwell, J. Cheng. "The Properties of Genome Conformation and Spatial Gene Interaction and Regulation Networks of Normal and Malignant Human Cell Types." PLoS ONE Vol. 8(3), 2013: e58793.
- Z. Wang, R. Cao, J. Cheng. "Three-Level Prediction of Protein Function by Combining Profile-Sequence Search, Profile-Profile Search, and Domain Co-occurrence Networks." BMC Bioinformatics Vol. 14(Suppl 3):S3, 2013:
My research interest is mainly focused on developing and applying machine learning and data mining techniques to solve biomedical problems, such as human genome data analysis and protein structure predictions. In addition, I am interested in promoting early engagement of undergraduate students (especially for women and underrepresented students) in machine learning, bioinformatics, and the data science field by interdisciplinary studies, and inspiring students to pursue advanced STEM education/research careers.