AI Insight
This review article examines how Natural Language Processing techniques, originally developed for analyzing human language, are being adapted to study biological sequences including DNA, RNA, and proteins. The authors survey various NLP approaches ranging from classical methods like word2vec to modern transformer-based models, evaluating how different tokenization strategies and model architectures perform on biological tasks such as protein structure prediction, gene expression analysis, and evolutionary studies. The review highlights both the potential and current limitations of applying language models to extract meaningful insights from large-scale genomic datasets.
Why it matters
The integration of NLP methods into bioinformatics could significantly accelerate our ability to decode biological information and understand fundamental life processes. These computational approaches may lead to practical advances in drug discovery, disease diagnosis, and personalized medicine by enabling more efficient analysis of the rapidly growing volumes of genomic data.
Understand the Science
arXiv:2506.02212v2 Announce Type: replace-cross
Abstract: Natural Language Processing (NLP) has transformed various fields beyond linguistics by applying techniques originally developed for human language to the analysis of biological sequences. This review explores the application of NLP methods to biological sequence data, focusing on genomics, transcriptomics, and proteomics. We examine how various NLP methods, from classic approaches like word2vec to advanced models employing transformers and hyena operators, are being adapted to analyze DNA, RNA, protein sequences, and entire genomes. The review also examines tokenization strategies and model architectures, evaluating their strengths, limitations, and suitability for different biological tasks. We further cover recent advances in NLP applications for biological data, such as structure prediction, gene expression, and evolutionary analysis, highlighting the potential of these methods for extracting meaningful insights from large-scale genomic data. As language models continue to advance, their integration into bioinformatics holds immense promise for advancing our understanding of biological processes in all domains of life.