Gene2Vec: Distributed Representation of Genes Based on Co-Expression
Open accessAbstract
Existing 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. From a pure data-driven fashion, we trained a 300 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 1.68X 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. Overall, we believe that this distributed representation of genes may be useful for more bioinformatics applications.
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Each human gene embedding can be represented by a 300-dimensional gene vector trained from 984 datasets of the GEO database based on gene co-expression patterns.
“Each of these human gene embeddings can be represented by a 300-dimensional gene vector trained from 984 datasets of the GEO database based on gene co-expression patterns [150].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [150]
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- Authors
- Du, J., Jia, P., Dai, Y., Tao, C., Zhao, Z., & Zhi, D.
- Corresponding author
- Du, Jingcheng
- Jia, Peilin
- Dai, Yulin
- Tao, Cui
- Zhao, Zhongming
- Zhi, Degui
- Venue
- BMC Genomics
- Publisher
- openRxiv
- Published
- 2018-03-22
- Volume
- 20
- Issue
- S1
- Pages
- 82-82
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC6360648
- OpenAlex
- W2952631022
- Cited by
- 204
- References
- 22
- Citations by year
- 2026: 21 · 2025: 43 · 2024: 36 · 2023: 21 · 2022: 22 · 2021: 27 · 2020: 22 · 2019: 9 · 2018: 2
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Cancer Research Institute
- Cancer Prevention and Research Institute of Texas (RP160015)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- gene embeddings, gene co-expression, pathway enrichment, tissue-specific genes, GEO database
- MeSH terms
- Algorithms, Epistasis, Genetic, Gene Expression Regulation, Humans, ROC Curve, Software, User-Computer Interface, Computational Biology, Gene Expression Profiling, Transcriptome
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