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

Abstract read

In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Soo Bin KwonBioinformatics Interdepartmental Program, University of California, Los Angeles, CA 90095, United States.ORCID 0000-0002-8670-0993

Jason ErnstBioinformatics Interdepartmental Program, University of California, Los Angeles, CA 90095, United States.ORCID 0000-0003-4026-7853

Funding

Spatiotemporal epigenomic and chromosomal architectural cell atlas of developing human brainsU01MH130995 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Chongyuan Luo, Mercedes Paredes · 2022 to 2026

$4.4M

NIMH NIH HHS U01 MH130995Rose Hills InnovatorUCLA Dissertation Year FellowshipUS National Institutes of Health DP1DA044371US National Institutes of Health U01HG012079US National Institutes of Health U01MH130995

6 · The paper itself

Abstract

motivationIdentifying 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.

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

Indexed as

EpigenomicsGenome, HumanTranscription FactorsBinding SitesHumansNeural Networks, ComputerProtein BindingSoftwareTranscription Factors

Identifiers

PMID41557834

DOI10.1093/bioinformatics/btag024

PMCPMC12910503

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.