Recent advances in deep learning and language models for studying the microbiome
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913503408816128 |
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| author | Yan, Binghao Nam, Yunbi Li, Lingyao Deek, Rebecca A. Li, Hongzhe Ma, Siyuan |
| author_facet | Yan, Binghao Nam, Yunbi Li, Lingyao Deek, Rebecca A. Li, Hongzhe Ma, Siyuan |
| contents | Recent advancements in deep learning, particularly large language models (LLMs), made a 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10579 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent advances in deep learning and language models for studying the microbiome Yan, Binghao Nam, Yunbi Li, Lingyao Deek, Rebecca A. Li, Hongzhe Ma, Siyuan Quantitative Methods Artificial Intelligence Machine Learning Recent advancements in deep learning, particularly large language models (LLMs), made a 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. |
| title | Recent advances in deep learning and language models for studying the microbiome |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.10579 |