Our lab's mission is to develop advanced machine learning algorithms that integrate multiple omics data (e.g. genetics, genomics, epigenetics, metabolomics), environmental exposures (e.g. allergens, viral infections, lung microbiome), images, and clinical characteristics to personalize treatments or phenotyping pulmonary diseases such as sarcoidosis, asthma, and ARDS. Our long-term research goal is to develop a comprehensive algorithm that can predict the treatment responses and can be applied to clinical practice.
Lab Resources & Services
The Liao laboratory has experience in big data analysis and bioinformatics and open to collaboration.
Biomarkers for ARDS Mortality Using Multi-Omics Approach
Sarcoidosis/Chronic Beryllium Disease GWAS
An Omics Precision Medicine Approach to Explore the Susceptibility and Phenotypes of Sarcoidosis
Nicholas Kenyon, MD Division Chief, Professor of Medicine Division of Pulmonary and Critical Care Medicine University of California-Davis