Precision Medicine and Therapeutic Discovery

Rapidly decreasing costs of molecular measurement technologies not only enable profiling of disease sample molecular features (e.g., transcriptome, proteome, metabolome) but also enable measuring of molecular signatures of individual drugs in clinically relevant models. We are interested in exploring new computational and statistical methods to relate diseases to potentially efficacious drugs through various molecular features.

People: Calvin Chi, Z. Tom Hu, Zoe Vernon, Yuting Ye

Collaborators: Professor Bin Chen’s Group

Selected Publications:

Predict Chemical States in Random Heterogenous Polymers

In this project, we are applying a Hidden Markov Model to statistically understand and predict the transmembrane segments in random heteropolymers (RHP) chains. We are now extending our Hidden Markov Model for large-scale hydrophobic states predictions by adapting variational inference and deep learning.

People: Yun Zhou

Collaborators: Professor Ting Xu’s Group

Selected Publications:

  • Single-Chain Heteropolymers Transport Protons Selectively and Rapidly, Tao Jiang, Aaron Hall, Marco Eres, Zahra Hemmatian, Baofu Qiao, Yun Zhou, Zhiyuan Ruan, Andrew D. Couse, William T. Heller, Haiyan Huang, Monica Olvera de la Cruz, Marco Rolandi, Ting Xu. Nature 2020 Jan; 577(7789):216-20.

  • Predicting Hydrophobic Segments by High Dimensional Inference and Machine Learning Approaches (In Preperation)

Bivariate Dependence Modeling and Biological Network Inference

Networks pervade many disciplines of science for analyzing complex systems with interacting components. In particular, this concept is commonly used to model interactions between genes and identify closely associated genes forming functional modules to help obtain a more complete portrayal of gene dynamics. Research along this line has been of our interest for a long time.

People: Soyeon Ahn, Zoe Vernon, Yuting Ye, Yun Zhou

Collaborators: Professor Peter Bickel, Professor. Marisa Wong Medina, Professor Noam Shomoron, Professor. YX Rachel Wang, Professor Michael S Waterman

Selected Publications: