Machine Learning Models to Predict Phenotype-Associated DNA Sequence Variation in Algae and Humans
Principal Investigator: Charles Danko
Baker Institute for Animal Health
Email: cgd24@cornell.edu
Sponsor: Sandia National Laboratories
Grant Number: 2423627
Title: Machine Learning Models to Predict Phenotype-Associated DNA Sequence Variation in Algae and Humans
Project Amount: $75,000
Project Period: October 2022 to September 2023
DESCRIPTION (provided by applicant):
In the era of genomics and big data, understanding how to use genomic information to predict outcomes is key to advancing the Department of Energy’s biology mission to ‘achieve a predictive understanding of complex biological, earth and environmental systems’ using synthetic biology. This proposal seeks to research and develop machine learning methods to predict the relationship between DNA sequences, gene expression and physiological outcomes. This work would both allow prediction of outcomes across biological systems based on their genome, and shed insight on how genome modification might affect these outcomes.