Discovery of Principles of Nature from Matrix and Tensor Modeling of Large-Scale Molecular Biological Data

• Activity Number: 348
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: WNAR
• Author(s): Orly Alter*
• Companies: University of Utah
• Keywords: Matrix and tensor decompositions ; singular value decomposition (SVD), generalized SVD (GSVD), higher-order GSVD, and tensor GSVD ; genomic signal processing ; personalized cancer diagnostics and prognostics ; from correlations to causal coordination ; from probability distribution functions to processes.

Abstract

I will describe the use of matrix and tensor decompositions in the simultaneous modeling of different types of large-scale molecular biological data, from different studies of cell division and cancer and from different organisms, to computationally predict previously unknown physical, cellular and evolutionary mechanisms that govern the activity of DNA and RNA. I will present novel multi-matrix and multi-tensor generalizations of the singular value decomposition as well as experimental verification and validation of some of the computational predictions. These models bring physicians a step closer to one day being able to predict and control the progression of cell division and cancer as readily as NASA engineers plot the trajectories of spacecraft today.

[1] P. Sankaranarayanan,* T. E. Schomay,* K. A. Aiello, and O. Alter, "Tensor GSVD of Patient- and Platform-Matched Tumor and Normal DNA Copy-Number Profiles Uncovers Chromosome Arm-Wide Patterns of Tumor-Exclusive Platform-Consistent Alterations Encoding for Cell Transformation and Predicting Ovarian Cancer Survival," PLoS One 10 (4), article e121396 (April 2015); http://dx.plos.org/10.1371/journal.pone.0121396