Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.
Dr. Orly Alter

Dr. Orly Alter - Associate Professor

USTAR Associate Professor of Bioengineering and Human Genetics
Track in Computational Systems and Synthetic Bioengineering

orly@sci.utah.edu
Genomic Signal Processing Lab


Publications
Photos: 1, 2, and 3

Background

Orly Alter is a USTAR associate professor of bioengineering and human genetics at the Scientific Computing and Imaging Institute1  and the Huntsman Cancer Institute at the University of Utah, and the principal investigator of an NCI Physical Sciences in Oncology U01 project grant.2,3  Inventor of the "eigengene,"4,5,6  she pioneered the matrix7,8,9  and tensor10,11,12,13,14,15  modeling of large-scale molecular biological data, which, as she demonstrated, can be used to correctly predict previously unknown physical,16,17,18  cellular,19,20,21,22,23  and evolutionary24,25  mechanisms.26,27,28  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,29,30,31  is recognized today as crucial to the field of gravitational wave detection.32,33

Research Interests

Postdoctoral and Graduate Positions at the Genomic Signal Processing Lab

NCI U01 CA-202144: Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics

In her Genomic Signal Processing Lab, Alter develops novel, multi-tensor generalizations of the singular value decomposition, and uses them in the comparisons of brain,34,35,36,37  lung,38  ovarian,39,40,41,42,43,44,45  and uterine cancer and normal genomes, to uncover patterns of DNA copy-number alterations that predict survival and response to treatment, statistically better than, and independent of, the best indicators in clinical use and existing laboratory tests. Recurring alterations have been recognized as a hallmark of cancer for over a century, and observed in these cancers' genomes for decades; however, copy-number subtypes predictive of patients' outcomes were not identified before. The data had been publicly available, but the patterns remained unknown until the data were modeled by using the multi-tensor decompositions, illustrating the universal ability of these decompositions—generalizations of the frameworks that underlie the theoretical description of the physical world—to find what other methods miss.