Deep learning methods in metagenomics: a review

Microbial Genomics, Год журнала: 2024, Номер 10(4)

Опубликована: Апрель 17, 2024

The ever-decreasing cost of sequencing and the growing potential applications metagenomics have led to an unprecedented surge in data generation. One most prevalent is study microbial environments, such as human gut. gut microbiome plays a crucial role health, providing vital information for patient diagnosis prognosis. However, analysing metagenomic remains challenging due several factors, including reference catalogues, sparsity compositionality. Deep learning (DL) enables novel promising approaches that complement state-of-the-art pipelines. DL-based methods can address almost all aspects analysis, pathogen detection, sequence classification, stratification disease prediction. Beyond generating predictive models, key aspect these also their interpretability. This article reviews DL metagenomics, convolutional networks, autoencoders attention-based models. These aggregate contextualized pave way improved care better understanding microbiome's our health.

Язык: Английский

Deep learning in microbiome analysis: a comprehensive review of neural network models

Frontiers in Microbiology, Год журнала: 2025, Номер 15

Опубликована: Янв. 22, 2025

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to integration deep learning (DL) methods. These computational techniques have become essential for addressing inherent complexity and high-dimensionality microbiome data, which consist different types omics datasets. Deep algorithms shown remarkable capabilities pattern recognition, feature extraction, predictive modeling, enabling researchers uncover hidden relationships within ecosystems. By automating detection functional genes, interactions, host-microbiome dynamics, DL methods offer unprecedented precision understanding composition its impact on health, disease, environment. However, despite their potential, approaches face challenges research. Additionally, biological variability datasets requires tailored ensure robust generalizable outcomes. As research continues generate vast complex datasets, these will be crucial advancing microbiological insights translating them into practical applications with DL. This review provides an overview models discussing strengths, uses, implications future studies. We examine how are being applied solve key problems highlight potential pathways overcome current limitations, emphasizing transformative could field moving forward.

Язык: Английский

Deep learning methods in metagenomics: a review

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Авг. 8, 2023

Abstract The ever-decreasing cost of sequencing and the growing potential applications metagenomics have led to an unprecedented surge in data generation. One most prevalent is study microbial environments, such as human gut. gut microbiome plays a crucial role health, providing vital information for patient diagnosis prognosis. However, analyzing metagenomic remains challenging due several factors, including reference catalogs, sparsity, compositionality. Deep learning (DL) enables novel promising approaches that complement state-of-the-art pipelines. DL-based methods can address almost all aspects analysis, pathogen detection, sequence classification, stratification, disease prediction. Beyond generating predictive models, key aspect these also their interpretability. This article reviews deep metagenomics, convolutional networks (CNNs), autoencoders, attention-based models. These aggregate contextualized pave way improved care better understanding microbiome’s our health. Author summary In study, we look at vast world research genetic material from environmental samples, spurred by increasing affordability technologies. Our particular focus microbiome, environment teeming with microscopic life forms central health well-being. navigating through amounts generated not easy task. Traditional hit roadblocks unique nature data. That’s where (DL), today well known branch artificial intelligence, comes in. techniques existing open up new avenues research. They’re capable tackling wide range tasks, identifying unknown pathogens predicting based on patient’s microbiome. article, provide very comprehensive review different DL strategies networks, We are convinced significantly enhance field analysis its entirety, paving more accurate and, ultimately, care. PRISMA augmented diagram illustrated Fig 1 .

Язык: Английский