In her research, Orly Alter develops multi-tensor AI/ML, which, as she prospectively and retrospectively experimentally validated, is uniquely able to discover accurate, precise, clinically actionable, and mechanistically interpretable predictors from small-cohort and noisy, high-dimensional multi-omic data. Alter is a USTAR associate professor at the Scientific Computing and Imaging Institute1 and the Huntsman Cancer Institute at the University of Utah, a scientific advisory board member of the NCI-DOE Cancer Moonshot collaboration on predictive oncology, and the CSO and a co-founder of Prism AI Therapeutics, Inc.2,3,4 As a genetics postdoctoral fellow at Stanford University, she invented the concept of the "eigengene," in the 7th most cited PNAS paper of the year 2000 and the 45th most cited PNAS paper of all time.5,6,7,8 Her Ph.D. thesis in applied physics at Stanford University was published by Wiley,9,10,11 and is recognized as crucial to gravitational wave detection and quantum computing.12,13,14
Alter formulates the comparative spectral decompositions, her
physics-inspired15
multi-tensor16,17,18
generalizations19,20,21,22
of the singular value decomposition, to (i) compare and integrate any data types, of any number and dimensions, and (ii) scale with data sizes. Her models (iii) are interpretable in terms of known biology and batch effects and (iv)
correctly23
predict24,25,26,27,28
previously unknown
mechanisms.29,30
Her prospective and retrospective
validation31,32,33,34,35,36
of a genome-wide pattern of DNA copy-number alterations in
brain
37,38,39,40
tumors proved that the models discover predictors of survival and response to treatment that are (v) the most accurate and precise, (vi) clinically actionable in the general population based upon as few as 50–100 patients, and (vii) are consistent across studies and over time. She discovered this, and patterns in
lung,41,42
nerve,43,44
ovarian,45,46,47,48,49,50
and uterine tumors, in public data. Such alterations were recognized in cancer, yet all other attempts to associate them with outcome failed, establishing that Alter's AI/ML is uniquely suited to personalized medicine.
