Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics

2025-06-02

AI Summary

Biological sequence analysis across multi-omics—genomics, transcriptomics, and proteomics—faces fundamental challenges in modeling DNA, RNA, and protein sequences with appropriate granularity, evolutionary awareness, and functional interpretability. Method: This study systematically investigates the adaptation mechanisms and application boundaries of NLP techniques to biological sequences. We comprehensively map the architectural evolution from word2vec to Transformer and Hyena-based models, propose a cross-scale tokenization strategy, and introduce a task-driven evaluation framework. Contribution/Results: Through empirical validation on structural prediction, functional annotation, and gene expression modeling, we quantitatively benchmark model performance across sequence modeling fidelity, evolutionary signal capture, and functional generalization. Our analysis identifies precise performance ceilings and domain-specific applicability for each architecture. The work establishes a methodology for AI-native biological sequence modeling, enabling a paradigm shift toward precision biology grounded in foundational language modeling principles.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Search and Optimization: Evolutionary Computation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Applying NLP to analyze biological sequences in genomics, transcriptomics, proteomics.
• Adapting NLP methods like transformers for DNA, RNA, protein analysis.
• Evaluating NLP's potential for structure prediction and evolutionary insights.

Innovation

• Applying NLP to biological sequence analysis
• Using transformers for DNA, RNA, protein analysis
• Tokenization strategies for genomic data processing