Deciphering genomic codes using advanced NLP techniques: a scoping review.

ABSTRACT:

Objectives

The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of Natural Language Processing (NLP) techniques, particularly Large Language Models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. This review aims to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.

Methods

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopu

SUBMITTER: Cheng S 

PROVIDER: S-EPMC11623714 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature