NLP techniques applied to biological sequence analysis reviewed

A recent review paper explores the application of Natural Language Processing (NLP) techniques to analyze biological sequence data, including genomics, transcriptomics, and proteomics. The paper details how various NLP methods, from word2vec to advanced transformer and hyena operator models, are adapted for DNA, RNA, and protein sequence analysis. It also discusses tokenization strategies, model architectures, and recent advances in predicting protein structure, gene expression, and evolutionary relationships. The integration of NLP into bioinformatics is highlighted as a promising avenue for understanding complex biological processes. AI

IMPACT This review highlights how NLP advancements can accelerate biological discovery by enabling deeper analysis of genetic and protein data.

RANK_REASON The cluster contains a review paper published on arXiv detailing the application of NLP to biological sequence analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• deoxyribonucleic acid
• Ella Rannon
• genome
• genomics
• hyena operators
• natural language processing
• Protein sequences & data analysis
• Proteomics
• ribonucleic acid
• transcriptomics
• transformers
• Word2vec

• paper
• other

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