# Learning a pairwise epigenomic and transcription factor binding association score across the human genome

## TL;DR

This paper introduces LEPAE, a method that uses neural networks to identify meaningful relationships between genomic regions using epigenomic and transcription factor data.

## Contribution

The novel contribution is the development of LEPAE, a neural network-based approach to quantify pairwise genomic associations using epigenomic and TF binding data.

## Key findings

- LEPAE captures biologically meaningful pairwise relationships between genomic loci.
- The method was applied to thousands of human datasets to generate association scores.
- LEPAE scores are expected to serve as a valuable resource for genomic research.

## Abstract

Identifying pairwise associations between genomic loci is an important challenge for which large and diverse collections of epigenomic and transcription factor (TF) binding data can potentially be informative.

We developed Learning Evidence of Pairwise Association from Epigenomic and TF binding data (LEPAE). LEPAE uses neural networks to quantify evidence of association for pairs of genomic windows from large-scale epigenomic and TF binding data along with distance information. We applied LEPAE using thousands of human datasets. We show using additional data that LEPAE captures biologically meaningful pairwise relationships between genomic loci, and we expect LEPAE scores to be a resource.

The LEPAE scores and the software are available at https://github.com/ernstlab/LEPAE.