Presenter: Orly Alter
University of Utah, Salt Lake City, UT, U.S.
orly@sci.utah.edu

I will, first, briefly review our matrix and tensor modeling of large-scale molecular biological data, which, as we demonstrated, can be used to correctly predict previously unknown physical, cellular, and evolutionary mechanisms that govern the activity of DNA and RNA. Second, I will describe our recent generalized singular value decomposition (GSVD) and tensor GSVD comparisons of the genomes of tumor and normal cells from the same sets of astrocytoma brain and, separately, ovarian cancer patients, which uncovered patterns of DNA copy-number alterations that are correlated with a patient's survival and response to treatment. Third, I will present our higher-order GSVD (HO GSVD) and tensor HO GSVD, the only mathematical frameworks that can create a single coherent model from, i.e., simultaneously find similarities and dissimilarities across multiple two- or higher-dimensional datasets, by extending the GSVD from two to more than two matrices or tensors.

Slides

Dr. Alter is a USTAR associate professor of bioengineering and human genetics at the Scientific Computing and Imaging Institute and the Huntsman Cancer Institute at the University of Utah, and the principal investigator of an NCI Physical Sciences in Oncology U01 project grant. Inventor of the "eigengene," she pioneered the matrix and tensor modeling of large-scale molecular biological data, which, as she demonstrated, can be used to correctly predict previously unknown cellular mechanisms. Dr. Alter received her Ph.D. in applied physics at Stanford University, and her B.Sc. magna cum laude in physics at Tel Aviv University. Her Ph.D. thesis on "Quantum Measurement of a Single System,” which was published by Wiley-Interscience as a book, is recognized today as crucial to the field of gravitational wave detection.

Last modified: Wednesday, June 28, 2017, 6:02 PM