Information Integration and Biological Discovery

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Launch presentation

About the speaker

John Quackenbush received his PhD in 1990 in theoretical physics from UCLA working on string theory models. Following two years as a postdoctoral fellow in physics, Dr. Quackenbush applied for and received a Special Emphasis Research Career Award from the National Center for Human Genome Research to work on the Human Genome Project. He spent two years at the Salk Institute working on developing physical maps of human chromosome 11 and two years at Stanford University working on new laboratory and computational strategies for sequencing the Human Genome. In 1997 he joined the faculty of The Institute for Genomic Research (TIGR) where his focus began to shift to post-genomic applications with an emphasis on microarray analysis. Using a combination of laboratory and computational approaches, Dr. Quackenbush and his group developed analytical methods based on integration of data across domains to learn biological meaning from high-dimensional data. Since joining the faculties of the Dana-Farber Cancer Institute and the Harvard School of Public Health in 2005, his work has increasingly focused on the analysis of human cancer and expanded to embrace systems-based approaches to understanding and modeling biological problems.

Abstract

Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and a realization that biological processes are driven by complex networks of interacting biological molecules. However, there is a gap between the gene lists emerging from genome sequencing projects and the network diagrams that are essential if we are to understand the link between genotype and phenotype. ‘Omic technologies were once heralded as providing a window into those networks, but so far their success has been limited. To circumvent these limitations, we developed a method that combines ‘omic data with other sources of information. Here we will present a number of approaches we have developed, including an integrated database that collects clinical, research, and public domain data and synthesizes it to drive discovery and an application of seeded Bayesian Network analysis applied to gene expression data that deduces predictive models of network response.

Launch presentation