Evidence map›Paper›PMID 39900765›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2025

A Selective Review of Network Analysis Methods for Gene Expression Data.

Rong Li, Huangdi Yi, Shuangge Ma

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 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

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

3 authors.

Rong LiDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Huangdi YiServier Pharmaceuticals, Boston, MA, USA.
Shuangge MaDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA. shuangge.ma@yale.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of high-throughput profiling techniques, gene expressions have drawn significant attention due to their important biological implications, widespread data availability, and promising biological findings. The complex interactions and regulations among genes naturally lead to a network structure, which can provide a global view of molecular mechanisms and biological processes. This chapter provides a selective overview of constructing gene expression networks and utilizing them in downstream analysis. It also includes a demonstrating example.

Indexed as

Computational BiologyGene Expression ProfilingGene Regulatory NetworksGene Expression RegulationHumansSoftwareNetwork-based analysisNetwork (graph) construction(un)directed graph

Identifiers

PMID39900765

What OpenQuestion holds

Textmetadata
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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.