Biology Methods and Protocols, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Abstract Integrating genomics with diverse data modalities has the potential to revolutionize personalized medicine. However, this integration poses significant challenges due fundamental differences in types and structures. The vast size of genome necessitates transformation into a condensed representation containing key biomarkers relevant features ensure interoperability other modalities. This commentary explores both conventional state-of-the-art approaches language modeling (GLM), focus on representing extracting meaningful from genomic sequences. We latest trends applying techniques sequence data, treating it as text modality. Effective feature extraction is essential enabling machine learning models effectively analyze large datasets, particularly within multimodal frameworks. first provide step-by-step guide various preprocessing tokenization techniques. Then we explore methods for tokens using frequency, embedding, neural network-based approaches. In end, discuss (ML) applications genomics, focusing classification, regression, processing algorithms, integration. Additionally, role GLM functional annotation, emphasizing how advanced ML models, such Bidirectional encoder representations transformers, enhance interpretation data. To best our knowledge, compile end-to-end analytic convert complex biologically interpretable information GLM, thereby facilitating development novel data-driven hypotheses.

Language: Английский