Department of Electrical & Computer Engineering
Using signal processing and machine learning tools, Dr. Xiaoning Qian and his team are working to decipher which genes are critical to understanding and predicting disease progression and how genetic differences and environmental stress change the living system. These answers would help biologists develop new disease management practices.
As biologists provide Qian with various affected gene data sets, he and his students develop models and algorithms to analyze the data. They seek to identify important genes, decode which are intertwined and understand which trigger system response, like in immune pathways.
Qian’s goal is to develop analytic methods that lead to biologically meaningful messages. This could ultimately help create user-friendly software for biologists.
Currently, the team is in the early stage of developing methods to effectively analyze genomic data. They are incorporating Bayesian methods, which integrate the uncertainty inherent in limited data samples, and attempting to reconstruct the dynamic system of cells based on their data analysis.
The methodology Qian is developing relies on big data set analyses and machine learning algorithms, which has application potential that extends well beyond genomics. Following this development, he will seek to have his findings validated across multiple studies.
Read the original story about Dr. Qian’s research.
