Evidence map›Paper›PMID 36711917›Full record

ArticlebioRxiv : the preprint server for biology2023

EpiMix: an integrative tool for epigenomic subtyping using DNA methylation.

Yuanning Zheng, John Jun, Kevin Brennan, Olivier Gevaert

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Yuanning ZhengStanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
John JunStanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Kevin BrennanStanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Olivier GevaertStanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Stanford University · US

Funding

Organoid-based Discovery of Oncogenic Drivers and Treatment Resistance MechanismsU01CA217851 · NCI · STANFORD UNIVERSITY · PI CURTIS, CHRISTINA N, JI, HANLEE P · 2017 to 2021
$4.7M
Modeling KRAS-Dependent Synthetic Lethality in Human Colon OrganoidsU01CA199241 · NCI · STANFORD UNIVERSITY · PI KUO, CALVIN J, SHOKAT, KEVAN M. · 2016 to 2019
$4.3M
Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocationR01CA260271 · NCI · STANFORD UNIVERSITY · PI GEVAERT, OLIVIER · 2021 to 2025
$3.1M
NCI NIH HHS R01 CA260271NCI NIH HHS U01 CA199241NCI NIH HHS U01 CA217851
6 · The paper itself

Abstract

DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer, immunological, and cardiovascular diseases. Recent technological advances enable genome-wide quantification of DNAme in large human cohorts. So far, existing methods have not been evaluated to identify differential DNAme present in large and heterogeneous patient cohorts. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared to existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. Using cell-type specific data from two separate studies, we discovered novel epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven non-coding RNAs in non-small cell lung cancer.

Identifiers

PMID36711917
PMCPMC9881910
OpenAlexW4313591779

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.