Genome analysis through image processing with deep learning models.

Journal

Journal of human genetics

ISSN: 1435-232X

Titre abrégé: J Hum Genet

Pays: England

ID NLM: 9808008

Informations de publication

Date de publication:
31 Jul 2024

Historique:

received: 08 01 2024

accepted: 08 07 2024

revised: 08 07 2024

medline: 1 8 2024

pubmed: 1 8 2024

entrez: 31 7 2024

Statut: aheadofprint

Résumé

Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as Chaos Game Representation (CGR) and read pileup images. With rapid advancements in deep learning (DL) methods within computer vision and natural language processing, there is growing interest in applying image-based DL methods to genomic sequence analysis. These methods involve encoding genomic information as images or integrating spatial information from images into the analytical process. In this review, we summarize three typical applications that use image processing with DL models for genome analysis. We examine the utilization and advantages of these image-based approaches.

Identifiants

DOI: 10.1038/s10038-024-01275-0 PMID: 39085457

pubmed: 39085457

doi: 10.1038/s10038-024-01275-0

pii: 10.1038/s10038-024-01275-0

doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

Informations de copyright

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