Evidence map›Paper›PMID 40045208›Full record

SynthesisBMC genomics2025

DSLE2 random-effects meta-analysis model for high-throughput methylation data.

Nan Wang, Yang Zhou, Fengping Zhu, Shuilin Jin

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC genomics, 2025. 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

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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

4 authors.

Nan WangSchool of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
Yang ZhouSchool of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
Fengping ZhuDepartment of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200040, China. fpzhu@fudan.edu.cn.
Shuilin JinSchool of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China. jinsl@hit.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the rapid development of high-throughput sequencing technology, high-throughput sequencing data has grown on a massive scale, leading to the emergence of multiple public databases, such as EBI and GEO. Conducting secondary mining of high-throughput sequencing data in these databases can yield more valuable insights. Meta-analysis can quantitatively combine high-throughput sequencing data from the the same topic. It increases the sample size for data analysis, enhances statistical power, and results in more consistent and reliable conclusions.

resultsThis study proposes a new between-study variance estimator

conclusionsThis paper propose the DSLE2 random-effects meta-analysis model based on new between-study variance estimator Em. The DSLE2 model performs well for methylation data.

Indexed as

DNA MethylationHigh-Throughput Nucleotide SequencingModels, StatisticalAlgorithmsHumansLung NeoplasmsParkinson DiseaseBetween-study varianceMeta-analysisMethylation sequencing dataRandom-effects model

Identifiers

PMID40045208
PMCPMC11884006

What OpenQuestion holds

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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.