Evidence map›Paper›PMID 30867673›Full record

ArticleAlgorithms for molecular biology : AMB2019

Semi-nonparametric modeling of topological domain formation from epigenetic data.

Emre Sefer, Carl Kingsford

Abstract read
In one paragraph

Article in Algorithms for molecular biology : AMB, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
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  6. Article
  7. Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
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  16. Deconvolution of Ensemble Chromatin Interaction Data Reveals the Latent Mixing Structures in Cell Subpopulations.Journal of computational biology : a journal of computational molecular cell biology · 2016
    Article
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.

Emre Sefer1Machine Learning Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213 USA.ORCID 0000-0002-9186-0270
Carl Kingsford2Computational Biology Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213 USA.

Funding

Algorithms for Managing Uncertainty in Chromosome Conformation Capture DataR01HG007104 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI KINGSFORD, CARLETON LEE · 2013 to 2015
$1.3M
Fast k-mer Counting to Quantify Gene Expression and Improve Genome AssemblyR21HG006913 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI KINGSFORD, CARLETON LEE · 2012 to 2014
$430k
NHGRI NIH HHS R01 HG007104NHGRI NIH HHS R21 HG006913
6 · The paper itself

Abstract

backgroundHi-C experiments capturing the 3D genome architecture have led to the discovery of topologically-associated domains (TADs) that form an important part of the 3D genome organization and appear to play a role in gene regulation and other functions. Several histone modifications have been independently associated with TAD formation, but their combinatorial effects on domain formation remain poorly understood at a global scale.

resultsWe propose a convex semi-nonparametric approach called

conclusionsOur approach,

Indexed as

CaptureChromatinConformationEpigenetic modificationsPredictionTopological domains

Identifiers

PMID30867673
PMCPMC6399866

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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.