Andy Pereira

The paper uses a network-based supervised machine learning framework to predict and rank transcription factors involved in drought tolerance in plants.

The paper develops a genome-scale regulatory network for Arabidopsis seed development, identifies key transcription factors, and provides a webtool for analyzing transcriptomes and predicting TF regulatory effects.

RECoN is a web resource that uses gene coexpression networks to analyze abiotic stress responses in rice, revealing functional and regulatory insights from new transcriptome datasets.