Insilico Research Group
McKinney Laboratory for Bioinformatics and In Silico Modeling
Insilico Reseach Group
The goal of the In Silico Research Group is to extract meaningful biological information from the noise in high-dimensional biological data. We augment standard approaches, which may miss interaction effects, with machine learning and systems-level network models of integrated data. We are particularly interested in developing systems/network models of human immune response to vaccines and neuropsychiatric disorders.
Our data-driven algorithms draw from the fields of machine learning, information theory, network theory, mathematical modeling, physics, and statistical learning. We develop algorithms to integrate static and time-course data, next-generation sequence, transcriptomic, structural and functional MRI, and genome-wide association data into mechanistic models for disease susceptibility prediction and identification of therapeutic targets.
Brett McKinney, Ph.D.
Undergrad lab members win prestigious awards: Caleb Lareau (Goldwater) and Tricity Andrew (NSF Fellowshihp)
Check out Epistasis Network Visualization (ENViz): Demo
Check out our under-development Insilico Galaxy: Demo
TUNWG Lab Materials - 6/7/13