Inferring gene-regulatory networks using epigenomic priors.

Thomas E Bartlett, Melodie Li, Chenyu Song, Yuche Gao, Qiulin Huang

Abstract read

In one paragraph

Article in iScience, 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

5 authors.

Thomas E BartlettDepartment of Statistical Science, University College London, London, UK.

Melodie LiDepartment of Statistical Science, University College London, London, UK.

Chenyu SongDepartment of Statistical Science, University College London, London, UK.

Yuche GaoDepartment of Statistical Science, University College London, London, UK.

Qiulin HuangHuman Embryo and Stem Cell Laboratory, The Francis Crick Institute, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We show improved accuracy in-silico of inference of gene-regulatory network (GRN) structure, resulting from the use of an epigenomic prior network. We demonstrate important use-cases of our proposed methodology by re-analyzing datasets from 12 different studies, including scRNA-seq, DNA methylation (DNAme), chromatin accessibility, and histone modification data. We find that DNAme data are very effective for inferring the epigenomic prior network, recapitulating known epigenomic network structure found previously from chromatin accessibility data. Furthermore, we find that inferring the epigenomic prior network from DNAme data reveals candidate TF

Indexed as

Biocomputational methoddata processing in systems biologyEpigeneticsgene network

Identifiers

PMID41953005

DOI10.1016/j.isci.2026.115165

PMCPMC13053756

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.