Evidence map›Paper›PMID 41168869›Full record

ArticleGenome biology2025

Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA.

Isabella A Boyle, Nayeem Akram Aquib, Mustafa Kocak, Randy Creasi, Philip Montgomery, Catarina D Campbell, Joshua M Dempster

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In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

7 authors.

Isabella A BoyleBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. iboyle@broadinstitute.org.
Nayeem Akram AquibBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Mustafa KocakBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Randy CreasiBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Philip MontgomeryBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Catarina D CampbellBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Joshua M DempsterBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Overrepresentation analysis is used to identify biological enrichment in a list of genes. Here, we introduce GeneTEA, a model that ingests free-text gene descriptions and incorporates natural language processing methods to learn a sparse gene-by-term embedding, which can be treated as a de novo gene set database. In benchmarks against existing overrepresentation analysis tools, only GeneTEA properly controls false discovery while consistently surfacing the most relevant biology, doing so with less redundancy. We show that the same approach can be applied to other organisms' genomes or compounds. Furthermore, we provide an interactive app and API for the trained GeneTEA model.

Indexed as

Computational BiologyNatural Language ProcessingSoftwareDatabases, GeneticHumansGene setsNatural language processingOverrepresentation analysis

Identifiers

PMID41168869
PMCPMC12577433

What OpenQuestion holds

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LicenceCC BY
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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.