ML for network based gene prioritization

Project Title:

• Systems Genetics Studies on Rice Genomes for Analysis of Grain Yield and Quality Under Heat Stress
  • Project PI: Andy Pereira, U Arkansas, Fayetteville
  • RII Track-2 FEC

Description

GRAiN is a R/Shiny based app that uses machine learning to predict Transcription Factors that likely regulate drought stree in rice. Initial nested cross-validation tests showed high accuracy in predicting known drought genes, while revealing some interesting characteristics of drought response in plants. Read masnucript