We focus on development and application of statistical and computational methodologies for functional genomics data such as gene expression (microarray and RNA-seq), chIP-Seq and proteomics. Applications of this work have included such complex traits as diabetes, cancer and alcoholism. There is also a strong interest in our group in network inference, particulalry for applications in cancer and infectious disease. Related to these analyses is a long standing interest in data cleaning and integration

A snapshot of some of our current projects:

BeatAML: An effective paradigm for accelerating development of clinical cancer therapeutics must entail an integrated approach in which functional assay-based target identification is supported with genomic and expression-based validation techniques. By integrating functional siRNA profiling of leukemia patient samples with Whole Exome and RNA-Seq, we hypothesize that we will significantly accelerate the pace of target identification and validation in AML.

Illuminating molecular targetable pathways in HNSCC: We propose to better employ existing drugs to define new agents and combinations of agents to treat HNSCC, a disease with unchanged survival rates for four decades in need of new approaches, tools and perspectives. To do this, we will leverage the computational approaches to mine the functional and genomic data that our group pioneered from the BEATAML study.

BD2K @OHSU: The focus of the BD2K program is to support the research and development of innovative and transforming approaches and tools to maximize and accelerate the integration of Big Data and data science into biomedical research. We hold 2 R25 BD2K grants that are focused on enhancing data science training in biomedical research, as well as supporting the development of new methods.