2019 EMANUEL & CAROL PARZEN PRIZE FOR STATISTICAL INNOVATION
Proudly Presented to
Chancellor’s Professor in the Department of Statistics & Electrical Engineering
& Computer Science at University of California Berkeley
“Iterative Random Forests (iRF)”
Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on random forests (RFs) and random intersection trees (RITs) and through extensive, biologically inspired simulations, we developed the iterative random forest algorithm (iRF) to seek predictable and stable high-order Boolean interactions. We demonstrate utility of iRF for high-order Boolean interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and red phenotype using UK BioBank data. The latter is proof-of-concept step towards suggesting gene variants behind cardiovascular phenotypes for single cell experiments as part of a Chan-Zuckerberg Biohub Intercampus Award to UC Berkeley, UCSF and Stanford. Finally, a connection is made between iRF and our PCS framework where PCS stands for predictability, computability and stability.
Thursday, September 5, 2019, 11:10 a.m. Hawking Auditorium