Recent advances in deep learning and language models for studying the microbiome

作     者：Yan, Binhao Nam, Yunbi Li, Lingyao Deek, Rebecca A. Li, Hongzhe Ma, Siyuan 

作者机构：Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States School of Information University of South Florida TampaFL United States Department of Biostatistics and Health Data Science University of Pittsburgh PittsburghPA United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Adversarial machine learning 

摘      要：Recent advancements in deep learning, particularly large language models (LLMs), made significant impact on how researchers study microbiome and metagenomics data. Microbial protein and genomic sequences, like natural languages, form a language of life, enabling the adoption of LLMs to extract useful insights from complex microbial ecologies. In this paper, we review applications of deep learning and language models in analyzing microbiome and metagenomics data. We focus on problem formulations, necessary datasets, and the integration of language modeling techniques. We provide an extensive overview of protein/genomic language modeling and their contributions to microbiome studies. We also discuss applications such as novel viromics language modeling, biosynthetic gene cluster prediction, and knowledge integration for metagenomics studies. © 2024, CC BY.