Project Objectives
Genotypes and Environments result in the Phenotypes of organisms
The GenoPhenoEnvo (GPE) project aims to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. The central hypothesis of this research is that deep learning algorithms analyzing biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. We envision that a timely investment in data science will push through a bottleneck in life science, accelerating discovery of gene-phenotype-environment relationships, and catalyzing a new computational discipline to uncover the complex “rules of life.”
Who We Are
We are a Collaborative Research Project spread out across four different institutions of higher learning.
Leadership Team
Pankaj Jaiswal -- Principal Investigator (lead), Professor, Oregon State University
Anne Thessen -- Principal Investigator (former-lead), Associate Professor, University of Colorado Anschutz
Arun Ross -- Principal Investigator, Professor, Michigan State University
Remco Chang -- Principal Investigator, Associate Professor, Tufts University
Bryan Heidorn -- Principal Investigator, Professor, University of Arizona
Tyson Lee Swetnam -- Co-Principal Investigator, Assistant Research Professor, University of Arizona
Funding
The GenoPhenoEnvo Project is supported by the National Science Foundation under HDR Awards 1939945, 1940059, 1940062, and 1940330. CyVerse is supported under award number DBI-1743442.
Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the National Science Foundation.