Evidence map›Paper›PMID 40983974›Full record

ArticleGenome biology2025

GeneRAIN: multifaceted representation of genes via deep learning of gene expression networks.

Zheng Su, Mingyan Fang, Andrei Smolnikov, Marcel E Dinger, Emily C Oates, Fatemeh Vafaee

Abstract read
PubMed Full text at PMC Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Zheng Su *School of Biotechnology and Biomolecular Sciences, Faculty of Science, The University of New South Wales, Sydney, NSW, 2052, Australia.
Mingyan Fang *BGI Research, Wuhan, 430074, China.
Andrei SmolnikovSchool of Biotechnology and Biomolecular Sciences, Faculty of Science, The University of New South Wales, Sydney, NSW, 2052, Australia.
Marcel E Dinger *School of Biotechnology and Biomolecular Sciences, Faculty of Science, The University of New South Wales, Sydney, NSW, 2052, Australia.
Emily C Oates *School of Biotechnology and Biomolecular Sciences, Faculty of Science, The University of New South Wales, Sydney, NSW, 2052, Australia.
Fatemeh Vafaee *School of Biotechnology and Biomolecular Sciences, Faculty of Science, The University of New South Wales, Sydney, NSW, 2052, Australia. f.vafaee@unsw.edu.au.

Funding

Medical Research Future Fund MRF2016906
6 · The paper itself

Abstract

We develop GeneRAIN, a suite of Transformer-based models that learn gene expression relationships from 410 K human bulk RNA-seq samples. Featuring a novel Binning-By-Gene normalization technique, our models capture diverse biological information beyond expression. We introduce GeneRAIN-vec, a multifaceted vectorized gene representation that outperforms those from existing models. We demonstrate knowledge transfer from protein-coding genes to Make 62.5 million biological attribute predictions for 13,030 long noncoding RNAs. This work advances Transformer and self-supervised deep learning applications to expression data, enhancing biological exploration.

Indexed as

Deep LearningGene ExpressionGene Regulatory NetworksRNA, Long NoncodingRNA-SeqHumansTranscriptomeRNA, Long NoncodingDeep learningGene embeddingGene expressionGene representationRNA-seqTransformer

Identifiers

PMID40983974
PMCPMC12455793

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.