Our lab in UC-Davis aims to develop combinatorial and machine learning algorithms to solve problems arising in molecular biology.
We are especially interested in developing algorithms to study the genomics of diseases. Some of the active projects in the lab are:
Structural variation (SV) discovery: Developing computational methods for the discovery of structural variation (SV) in whole-genome sequenced (WGS) samples
Predicting the impact of SVs on 3D genome structure: Developing novel methods for predicting alteration to 3D genome structure caused by any type of SVs.
Predicting the impact of SVs on gene regulation and expression: Developing methods for predicting the impact of SVs on gene expression.
Biomolecular module discovery: Developing computational algorithms to discover modules and pathways in complex disorders (e.g., autism and cancer)
Early prediction and classification of complex disorders: Developing novel machine learning algorithms for early prediction of complex disease.
