Gene2vec: gene subsequence embedding for prediction of mammalian N 6 -methyladenosine sites from mRNA
Open accessAbstract
N 6 -Methyladenosine (m 6 A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N 6 -methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m 6 A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m 6 A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m 6 A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m 6 A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the state-of-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server.malab.cn/Gene2vec/ .
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scBERT embeds each gene as the sum of a gene2vec embedding and an expression embedding representing the gene's normalized, binned log-scale expression level.
“scBERT [7] embeds each gene as the sum of two embeddings: one representing the gene's normalized, binned log-scale expression level, and a second which specifies the gene identity via the previously developed gene2vec [12] embedding, allowing the model to differentiate genes in the otherwise order-less data.”
From A Deep Dive into Single-Cell RNA Sequencing Foundation Models (2023) · cites this paper as [12]
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A Deep Dive into Single-Cell RNA Sequencing Foundation Models
Boiarsky, R., Singh, N., Buendia, A., Getz, G., & Sontag, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.19.563100 · Cited by 43 · see citing works
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- Authors
- Zou, Q., Xing, P., Wei, L., & Liu, B.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Tianjin University (CN)
- Harbin Institute of Technology (CN)
- Venue
- RNA
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2018-11-13
- Volume
- 25
- Issue
- 2
- Pages
- 205-218
- ISSN
- 1355-8382 (ISSN-L)
- 1469-9001
- PMC
- PMC6348985
- OpenAlex
- W2900694973
- Cited by
- 525
- References
- 69
- Citations by year
- 2026: 13 · 2025: 43 · 2024: 59 · 2023: 35 · 2022: 100 · 2021: 147 · 2020: 73 · 2019: 54 · 2018: 1
- FWCI
- 24.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- RNA (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Key R&D Program of China (SQ2018YFC090002)
- Natural Science Foundation of China (61771331, 61822306, 61672184)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA modifications and cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- N6-methyladenosine, m6A site prediction, mRNA, word embeddings, convolutional neural networks, soft voting, epitranscriptomic modifications, cross-species prediction
- MeSH terms
- Machine Learning, Adenosine, Base Sequence, Models, Theoretical, RNA, Messenger, Neural Networks, Computer, Sequence Analysis, RNA, Computational Biology, High-Throughput Nucleotide Sequencing
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