About Me

I received my M.S. in Electrical Engineering from the Texas A&M University this Auguest. I am currently looking for a Ph.D. position in Computational Biology/Bioinformatics.

During my master’s program, I had been a graduate Research Assistant at Genetic Signaling Processing lab for two years. My research mentor was Dr. Yang Shen. I have studied and developed: (1) novel algorithms for protein complex modeling and docking based on normal mode analysis, (2) a framework to investigate how cancer propagates mutational effects through Protein-Protein Interaction networks and (3) machine learning algorithms and dynamic Bayesian network which predict clinical outcomes in DREAM and CAGI challenges.

My research interests lie in designing computational models to decipher biological mechanisms in complex genetic diseases and explore novel clinical treatments.

Download my CV here.

Chen, H., Sun, Y., & Shen, Y. (2017). Predicting protein conformational changes for unbound and homology docking: learning from intrinsic and induced flexibility. Proteins: Structure, Function, and Bioinformatics, 85(3), 544-556.

Knijnenburg, T. A., …, Shen, Y., Karimi, M., Chen, H. … (2018). Genomic and molecular landscape of DNA damage repair deficiency across The Cancer Genome Atlas. Cell Reports, 23(1), 239.

Menden, M. P., …, AstraZeneca-Sanger Drug Combination DREAM Consorti, … (2018). A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction. bioRxiv, 200451.

Chen, H., Hu, Q. (2014) Impact of thermal resistivity value of materials on calculation for conductor temperature of 10 kV three-core cable. Guangdong Electric Power, 27(4) 86-90.

Karimi, M., Sun, Y., Cao, Y., Chen, H., Moronfoye, O., & Shen, Y. (2018). Predicting missense mutational effects on protein functions and cancer pathogenicity. Submitted

Sun, Y., Chen, H., & Shen Y. (2018) PredPro: Predicting Protein conformational change and assessing rigidity assumption. Manuscript in preparation.

Chen, H., Shen Y. (2018). Interpreting system-level cancer mechanisms through machine learning. Manuscript in preparation.