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GEN’s editorial staff interviews life science academic and biotech industry leaders on important research, technology, and trends. These podcasts will keep you informed with all the important details you need.

Researchers at Duke University's Institute for Genome Sciences & Policy (IGSP) have developed a new method that essentially does for the genetic pathways underlying cancer what social networking web sites can do for people: It finds the connections among them. The team reported its findings in PLoS Computational Biology on Feb. 15.

During this weeks podcast, Dr. Sayan Mukherjee, an IGSP investigator and assistant professor in Duke's department of statistical science, and graduate student Elena Edelman, provide details on their novel technique and talk about what they were able to specifically demonstrate. Noting that their major innovation was the use of gene sets rather than just single genes in modeling tumor progression, the scientists describe why this was such a productive approach. They go on to explain how their modeling method enabled their team to characterize gene networks as they evolve over the course of tumor progression. The researchers also discuss the particular types of cancer that were amenable to analysis by the new approach and why.
Sayan Mukherjee Sayan Mukherjee, PhD joined the IGSP in 2004, where he works with the Center for Applied Genomics & Technology and the Center for Bioinformatics & Computatonal Biology. He is an Assistant Professor in the Department of Statistical Science.
He received a PhD from the Massachusetts Institute of Technology in 2001 and was a postdoctoral Fellow joint between the Whitehead Institute and the Center for Biological and Computational Learning, both at MIT. Dr. Mukherjee has been developing computational tools and infrastructure to analyze genomic data, specifically gene expression data.

3rd year PhD student

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