ReviewComputational and structural biotechnology journal2021
Representation learning applications in biological sequence analysis.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 89 citations in OpenAlex.
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- LGLoc as a new language model-driven graph neural network for mRNA localization.Scientific reports · 2025Article
- Deciphering genomic codes using advanced natural language processing techniques: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2025Article
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- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
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- MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm.PLoS computational biology · 2024Article
- StructmRNA a BERT based model with dual level and conditional masking for mRNA representation.Scientific reports · 2024Article
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- Phylogeny-aware linear B-cell epitope predictor detects targets associated with immune response to orthopoxviruses.Briefings in bioinformatics · 2024Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
- CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network.Journal of chemical information and modeling · 2024Article
- Cancer mutational signatures identification in clinical assays using neural embedding-based representations.Cell reports. Medicine · 2024Article
- Protein sequence analysis in the context of drug repurposing.BMC medical informatics and decision making · 2024Article
- Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.Bioinformatics (Oxford, England) · 2024Article
- Integrated convolution and self-attention for improving peptide toxicity prediction.Bioinformatics (Oxford, England) · 2024Article
- Deep generative design of RNA family sequences.Nature methods · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention. In this method, biological sequences are regarded as sentences while the single nucleic acids/amino acids or k-mers in these sequences represent the words. Embedding is an essential step in NLP, which performs the conversion of these words into vectors. Specifically, representation learning is an approach used for this transformation process, which can be applied to biological sequences. Vectorized biological sequences can then be applied for function and structure estimation, or as input for other probabilistic models. Considering the importance and growing trend for the application of representation learning to biological research, in the present study, we have reviewed the existing knowledge in representation learning for biological sequence analysis.
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Registered trials
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 Socratic graph.