Prediction of whole-cell transcriptional response with machine learning
Abstract
Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 27, 2021
- Source ID
- 10.1093/bioinformatics/btab676
Entities
People
- Alexander Cristofaro
- Amin Espah Borujeni
- Carolyn Corbet
- Christopher Voigt
- D. Benjamin Gordon
- Diveena Becker
- Enoch Yeung
- George Zheng
- Hamed Eramian
- Hamid Doost Hosseini
- Jedediah Singer
- Joe Stubbs
- John Fonner
- Joshua Urrutia
- Katie Clowers
- Mark Weston
- Matthew Vaughn
- Mohammed Eslami
- Niall Gaffney
- Paul Maschhoff
- Yuval Dorfan
Organizations
- Air Force Research Laboratory
- Defense Advanced Research Projects Agency
- Massachusetts Institute of Technology
- Texas Advanced Computing Center
- United States Department of Defense
- University of California, Santa Barbara