ArticleEvolutionary bioinformatics online2019
Feature Learning of Virus Genome Evolution With the Nucleotide Skip-Gram Neural Network.
Article in Evolutionary bioinformatics online, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Immortal AI, Mortal Life: Long-Term Perspectives on AI and Human Knowledge.Journal of medical Internet research · 2026Article
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- Nanopore sequencing of protozoa: Decoding biological information on a string of biochemical molecules into human-readable signals.Computational and structural biotechnology journal · 2025Article
- A pangenome analysis of ESKAPE bacteriophages: the underrepresentation may impact machine learning models.Frontiers in molecular biosciences · 2024Article
- Three Innovations of Next-Generation Antibiotics: Evolvability, Specificity, and Non-Immunogenicity.Antibiotics (Basel, Switzerland) · 2023Review
- In silico optimization of RNA-protein interactions for CRISPR-Cas13-based antimicrobials.Biology direct · 2022Article
- Rethinking Protein Drug Design with Highly Accurate Structure Prediction of Anti-CRISPR Proteins.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Diverse ATPase Proteins in Mobilomes Constitute a Large Potential Sink for Prokaryotic Host ATP.Frontiers in microbiology · 2021Article
- Representation learning applications in biological sequence analysis.Computational and structural biotechnology journal · 2021Review
- Review
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Authors and funding
1 author.
Funding
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Abstract
Recent studies reveal that even the smallest genomes such as viruses evolve through complex and stochastic processes, and the assumption of independent alleles is not valid in most applications. Advances in sequencing technologies produce multiple time-point whole-genome data, which enable potential interactions between these alleles to be investigated empirically. To investigate these interactions, we represent alleles as distributed vectors that encode for relationships with other alleles in the course of evolution and apply artificial neural networks to time-sampled whole-genome datasets for feature learning. We build this platform using methods and algorithms derived from natural language processing (NLP), and we denote it as the nucleotide skip-gram neural network. We learn distributed vectors of alleles using the changes in allele frequency of echovirus 11 in the presence or absence of the disinfectant (ClO
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Registered trials
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