Medicine and Health

PEACOCK: a machine learning approach to assess the validity of cell type-specific enhancer-gene regulatory relationships

C. Mills, C. N. Marconett, et al.

This cutting-edge research by Caitlin Mills, Crystal N. Marconett, Juan Pablo Lewinger, and Huaiyu Mi presents PEACOCK, a revolutionary machine learning tool that predicts cell type-specific enhancer-gene relationships. By utilizing validated enhancer-gene links, this model offers a powerful method to advance our understanding of gene regulation in diseases.

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Playback language: English

Index

Abstract

Identifying target genes of enhancers is crucial for understanding their role in diseases. This study developed PEACOCK, a machine learning approach using experimentally validated enhancer-gene links and publicly available data to predict cell type-specific enhancer-gene regulatory relationships. The model was trained and validated across four cell lines, and the resulting scores were incorporated into the PEREGRINE database. These scores provide a quantitative framework for enhancer-gene regulatory prediction, facilitating downstream analyses and experimental validation.

Publisher

npj Systems Biology and Applications

Published On

Apr 03, 2023

Authors

Caitlin Mills, Crystal N. Marconett, Juan Pablo Lewinger, Huaiyu Mi

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