Predicting patient-specific enhancer-promoter interactions.

Brittany Baur, Sushmita Roy

Abstract readComment

In one paragraph

Article in Cell reports methods, 2023. 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

Comment on
Recapitulation of patient-specific 3D chromatin conformation using machine learning.2023

5 · Who and what money

Authors and funding

2 authors.

Brittany BaurWisconsin Institute for Discovery, 330 N. Orchard Street, Madison, WI 53715, USA; The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, USA; Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.

Sushmita RoyWisconsin Institute for Discovery, 330 N. Orchard Street, Madison, WI 53715, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, USA. Electronic address: sroy@biostat.wisc.edu.

Funding

Leveraging multi-species single cell omic datasets to study the evolution of cell type-specific gene regulatory networksR01HG012349 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI ROY, SUSHMITA · 2022 to 2025

$1.8M

Computational approaches for comparative regulatory genomics to decipher long-range gene regulationR01HG010045 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI ROY, SUSHMITA · 2018 to 2021

$1.4M

NHGRI NIH HHS R01 HG010045NHGRI NIH HHS R01 HG012349

6 · The paper itself

Abstract

Computational methods that can predict hard-to-measure modalities from those that are easier to measure, in a patient-specific manner, play a critical role in personalized medicine. In this issue of Cell Reports Methods, Khurana et al. present differential gene targets of accessible chromatin (DGTAC), an approach which predicts patient-specific enhancer-promoter interactions.

Indexed as

ChromatinRegulatory Sequences, Nucleic AcidHumansPatientsPrecision MedicinePromoter Regions, GeneticChromatin

Identifiers

PMID37751694

DOI10.1016/j.crmeth.2023.100594

PMCPMC10545932

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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.