Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease
The human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-res...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/6/5229 |
| _version_ | 1797611304371879936 |
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| author | Mauro Giuffrè Rita Moretti Claudio Tiribelli |
| author_facet | Mauro Giuffrè Rita Moretti Claudio Tiribelli |
| author_sort | Mauro Giuffrè |
| collection | DOAJ |
| description | The human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-resolution data. The vast amount of data generated by these methods has led to the development of computational methods for data processing and analysis, with machine learning becoming a powerful and widely used tool in this field. Despite the promising results of machine learning-based approaches for analyzing the association between microbiota and disease, there are several unmet challenges. Small sample sizes, disproportionate label distribution, inconsistent experimental protocols, or a lack of access to relevant metadata can all contribute to a lack of reproducibility and translational application into everyday clinical practice. These pitfalls can lead to false models, resulting in misinterpretation biases for microbe–disease correlations. Recent efforts to address these challenges include the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks; implementation of these efforts has facilitated a shift in the field from observational association studies to experimental causal inference and clinical intervention. |
| first_indexed | 2024-03-11T06:27:00Z |
| format | Article |
| id | doaj.art-5ccac75ebea8476aaddbaa642385f16d |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T06:27:00Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-5ccac75ebea8476aaddbaa642385f16d2023-11-17T11:30:56ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-03-01246522910.3390/ijms24065229Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and DiseaseMauro Giuffrè0Rita Moretti1Claudio Tiribelli2Department of Medical, Surgical and Health Sciences, University of Trieste, 34149 Trieste, ItalyDepartment of Medical, Surgical and Health Sciences, University of Trieste, 34149 Trieste, ItalyFondazione Italiana Fegato-Onlus, The Liver-Brain Unit “Rita Moretti”, 34149 Trieste, ItalyThe human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-resolution data. The vast amount of data generated by these methods has led to the development of computational methods for data processing and analysis, with machine learning becoming a powerful and widely used tool in this field. Despite the promising results of machine learning-based approaches for analyzing the association between microbiota and disease, there are several unmet challenges. Small sample sizes, disproportionate label distribution, inconsistent experimental protocols, or a lack of access to relevant metadata can all contribute to a lack of reproducibility and translational application into everyday clinical practice. These pitfalls can lead to false models, resulting in misinterpretation biases for microbe–disease correlations. Recent efforts to address these challenges include the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks; implementation of these efforts has facilitated a shift in the field from observational association studies to experimental causal inference and clinical intervention.https://www.mdpi.com/1422-0067/24/6/5229gut microbiotagut microbiomehealthmicrobiomeeubiosisdysbiosis |
| spellingShingle | Mauro Giuffrè Rita Moretti Claudio Tiribelli Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease International Journal of Molecular Sciences gut microbiota gut microbiome health microbiome eubiosis dysbiosis |
| title | Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease |
| title_full | Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease |
| title_fullStr | Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease |
| title_full_unstemmed | Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease |
| title_short | Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease |
| title_sort | gut microbes meet machine learning the next step towards advancing our understanding of the gut microbiome in health and disease |
| topic | gut microbiota gut microbiome health microbiome eubiosis dysbiosis |
| url | https://www.mdpi.com/1422-0067/24/6/5229 |
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