Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics
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
The rapid advancements in large language models and transformer architectures are enabling novel applications of NLP beyond traditional text analysis into complex biological data.
This convergence of NLP and biological sciences promises to accelerate discovery in genomics, transcriptomics, and proteomics, leading to new insights into life itself and novel biotechnological applications.
The analytical toolkit for biological sequence data is expanding significantly, allowing for more sophisticated pattern recognition and predictive modeling than previously possible.
- · Biotechnology sector
- · Pharmaceutical companies
- · AI research labs
- · Genomic sequencing companies
Accelerated drug discovery and development for complex diseases will likely occur.
Personalized medicine approaches could become far more sophisticated and widespread due to enhanced biological data interpretation.
The ability to 'program' biological systems might advance significantly, blurring the lines between natural and engineered life forms.
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Read at arXiv cs.AI