Netherlands Bioinformatics Conference 2014

Netherlands Bioinformatics Conference 2014

Ilya Shmulevich

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CV

Ilya Shmulevich received his Ph.D. in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, in 1997. His graduate research was in the area of nonlinear signal processing, with a focus on the theory and design of nonlinear digital filters, Boolean algebra, lattice theory, and applications to music pattern recognition. From 1997-1998, he was a postdoctoral researcher at the Nijmegen Institute for Cognition and Information at the University of Nijmegen and National Research Institute for Mathematics and Computer Science at the University of Amsterdam in The Netherlands, where he studied computational models of music perception and recognition, focusing on tonality induction and rhythm complexity. In 1998-2000, he worked as a senior researcher at the Tampere International Center for Signal Processing at the Signal Processing Laboratory in Tampere University of Technology, Tampere, Finland. While in Tampere, he did research in nonlinear systems, image recognition and classification, image correspondence, computational learning theory, multiscale and spectral methods, and statistical signal processing.

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Abstract

Title: Integrative Analysis of Data from The Cancer Genome Atlas

Abstract

The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to improve our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. I will describe our efforts, as a Genome Data Analysis Center within TCGA, to integrate the highly heterogeneous molecular and clinical data collected from thousands of cancer patients spanning over 30 tumor types. This work involves the identification of statistical associations in the data and the development of web-based tools to interactively explore these associations. We further integrate the interdependencies in the data with other information from public biomedical resources by constructing and analyzing large heterogeneous graphs. These analyses are helping to accelerate the scientific progress of the disease working groups in TCGA and are providing unprecedented opportunities to use these comprehensive data sets for clinical and therapeutic applications, with the ultimate goal of improving our ability to diagnose, treat and prevent cancer.

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