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
Genomic Signal Processing Lab

Photos: 1, 2, and 3


Orly 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.1  She completed her DOE/Sloan Foundation postdoctoral fellowship at the Department of Genetics at Stanford University, and 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,2,3,4  is recognized today as crucial to the field of gravitational wave detection.5,6  

Alter is the principal investigator of a five-year, three million-dollar National Cancer Institute (NCI) Physical Sciences in Oncology U01 project grant.7,8  Additional support for her work comes from the Utah Science, Technology, and Research (USTAR) Initiative. Completed project grants include an NSF CAREER Award,9  and National Human Genome Research Institute (NHGRI) R01 and K01 awards.

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 the Genomic Signal Processing Lab, we are breaking new ground in mathematics, genetics, and at the interface between the two fields, since our highly cited10  invention of the "eigengene."11,12

At the interface, we pioneered both matrix13,14,15  and tensor16,17  modeling of large-scale molecular biological data, which, as we demonstrated, can be used to correctly predict previously unknown physical,18,19,20  cellular,21,22,23,24,25  and evolutionary26,27  mechanisms that govern the activity of DNA and RNA.28,29,30

In mathematics, we developed the only framework that can create a single coherent model from multiple two-dimensional datasets, by extending the generalized singular value decomposition (GSVD) from two to more than two matrices.31,32,33

In genetics, our recent GSVD and tensor GSVD comparisons of the genomes of tumor and normal cells from the same sets of glioblastoma and lower-grade astrocytoma brain34,35,36  and, separately, ovarian37,38,39,40,41,42,43  cancer patients uncovered patterns of DNA copy-number alterations that were found to be correlated with a patient's survival and response to chemotherapy. For three decades prior, the best predictor of ovarian cancer survival was the tumor's stage; more than a quarter of ovarian tumors are resistant to the platinum-based chemotherapy, the first-line treatment, yet no diagnostic existed to distinguish resistant from sensitive tumors before the treatment. For five decades prior, the best prognostic indicator of glioblastoma was the patient's age at diagnosis. The ovarian and brain cancer data were published, but the patterns remained unknown until we applied our mathematical frameworks.