Our lab is interested in developing and applying innovative statistical and bioinformatics methodology to facilitate translational genomic research from bench to clinic. In particular, we are interested in biomarker discovery, predictive modeling, subtype identification and integrative analysis of multi-omics data, with application to cancers and neurodegenerative diseases.


  1. Integrative Statistical Models for TNBC Biomarker Discovery

  2. Integrative Prediction Models for Metastasis Risk in Colon Cancer

  3. IPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC

  4. New Computational Tools for Understanding and Predicting AD via Age- associated DNA Methylation  Changes

  5. Building Blood Based DNA Methylation Signatures for AD That Are Reflective of CNS Changes

  6. Integrative Genomic Approaches for Understanding Sex Differences in AD


Funding Sources