ArticleBMC genomics2019
Gene2vec: distributed representation of genes based on co-expression.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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93 citing papers in PubMed.
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- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
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- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Uncertainty-aware synthetic lethality prediction with pretrained foundation models.bioRxiv : the preprint server for biology · 2026Article
- scLong: a billion-parameter foundation model for capturing long-range gene context in single-cell transcriptomics.Nature communications · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- A multimodal knowledge-enhanced whole-slide pathology foundation model.Nature communications · 2025Article
- A DSSM network for inferring and prioritizing cell-type-specific regulons using single-cell RNA-seq data.BMC bioinformatics · 2025Article
- Heimdall: A Modular Framework for Tokenization in Single-Cell Foundation Models.bioRxiv : the preprint server for biology · 2025Article
- In silico biological discovery with large perturbation models.Nature computational science · 2025Article
- Transformer-based representation learning for robust gene expression modeling and cancer prognosis.Scientific reports · 2025Article
- UNICORN: Towards universal cellular expression prediction with a multi-task learning framework.Nature communications · 2025Article
- Cell-GraphCompass: modeling single cells with graph structure foundation model.National science review · 2025Article
33 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
backgroundExisting functional description of genes are categorical, discrete, and mostly through manual process. In this work, we explore the idea of gene embedding, distributed representation of genes, in the spirit of word embedding.
resultsFrom a pure data-driven fashion, we trained a 200-dimension vector representation of all human genes, using gene co-expression patterns in 984 data sets from the GEO databases. These vectors capture functional relatedness of genes in terms of recovering known pathways - the average inner product (similarity) of genes within a pathway is 1.52X greater than that of random genes. Using t-SNE, we produced a gene co-expression map that shows local concentrations of tissue specific genes. We also illustrated the usefulness of the embedded gene vectors, laden with rich information on gene co-expression patterns, in tasks such as gene-gene interaction prediction.
conclusionsWe proposed a machine learning method that utilizes transcriptome-wide gene co-expression to generate a distributed representation of genes. We further demonstrated the utility of our distribution by predicting gene-gene interaction based solely on gene names. The distributed representation of genes could be useful for more bioinformatics applications.
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