Evidence map›Paper›PMID 42681538›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Graph-Based Gene Embedding.

Henry Cousins

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. 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

1 author.

Henry CousinsDepartment of Ophthalmology, Harvard Medical School, Boston, MA, 02114, USA. hcousins@mgb.org.ORCID http://orcid.org/0000-0002-8694-0604

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene embeddings, concise numerical descriptions of complex genetic relationships, are powerful tools for predicting unknown functions of genes and drugs. The application of graph-specific representation learning algorithms on large biological network databases allows for the efficient computation of highly expressive gene embeddings. This chapter provides an explanation of the end-to-end calculation of such embeddings, with a focus on the most widely used and generally applicable datasets and algorithms.

Indexed as

Computational BiologyGene Regulatory NetworksAlgorithmsDatabases, GeneticHumansMachine LearningBioinformaticsGene embeddingMachine learningNetwork biologyProtein–protein interactions

Identifiers

PMID42681538

What OpenQuestion holds

Textmetadata
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.