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Roy G, Prifti E, Belda E, Zucker JD. Deep learning methods in metagenomics: a review. Microb Genom 2024; 10:001231. [PMID: 38630611 PMCID: PMC11092122 DOI: 10.1099/mgen.0.001231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
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Affiliation(s)
- Gaspar Roy
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
| | - Edi Prifti
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l’hopital, 75013 Paris, France
| | - Eugeni Belda
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l’hopital, 75013 Paris, France
| | - Jean-Daniel Zucker
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l’hopital, 75013 Paris, France
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Angelova IY, Kovtun AS, Averina OV, Koshenko TA, Danilenko VN. Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning. Int J Mol Sci 2023; 24:16459. [PMID: 38003647 PMCID: PMC10671666 DOI: 10.3390/ijms242216459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
In the last few years, investigation of the gut-brain axis and the connection between the gut microbiota and the human nervous system and mental health has become one of the most popular topics. Correlations between the taxonomic and functional changes in gut microbiota and major depressive disorder have been shown in several studies. Machine learning provides a promising approach to analyze large-scale metagenomic data and identify biomarkers associated with depression. In this work, machine learning algorithms, such as random forest, elastic net, and You Only Look Once (YOLO), were utilized to detect significant features in microbiome samples and classify individuals based on their disorder status. The analysis was conducted on metagenomic data obtained during the study of gut microbiota of healthy people and patients with major depressive disorder. The YOLO method showed the greatest effectiveness in the analysis of the metagenomic samples and confirmed the experimental results on the critical importance of a reduction in the amount of Faecalibacterium prausnitzii for the manifestation of depression. These findings could contribute to a better understanding of the role of the gut microbiota in major depressive disorder and potentially lead the way for novel diagnostic and therapeutic strategies.
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Affiliation(s)
- Irina Y. Angelova
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), 119333 Moscow, Russia; (A.S.K.); (O.V.A.); (V.N.D.)
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Fung DLX, Li X, Leung CK, Hu P. A self-knowledge distillation-driven CNN-LSTM model for predicting disease outcomes using longitudinal microbiome data. BIOINFORMATICS ADVANCES 2023; 3:vbad059. [PMID: 37228387 PMCID: PMC10203376 DOI: 10.1093/bioadv/vbad059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/03/2023] [Accepted: 05/01/2023] [Indexed: 05/27/2023]
Abstract
Motivation Human microbiome is complex and highly dynamic in nature. Dynamic patterns of the microbiome can capture more information than single point inference as it contains the temporal changes information. However, dynamic information of the human microbiome can be hard to be captured due to the complexity of obtaining the longitudinal data with a large volume of missing data that in conjunction with heterogeneity may provide a challenge for the data analysis. Results We propose using an efficient hybrid deep learning architecture convolutional neural network-long short-term memory, which combines with self-knowledge distillation to create highly accurate models to analyze the longitudinal microbiome profiles to predict disease outcomes. Using our proposed models, we analyzed the datasets from Predicting Response to Standardized Pediatric Colitis Therapy (PROTECT) study and DIABIMMUNE study. We showed the significant improvement in the area under the receiver operating characteristic curve scores, achieving 0.889 and 0.798 on PROTECT study and DIABIMMUNE study, respectively, compared with state-of-the-art temporal deep learning models. Our findings provide an effective artificial intelligence-based tool to predict disease outcomes using longitudinal microbiome profiles from collected patients. Availability and implementation The data and source code can be accessed at https://github.com/darylfung96/UC-disease-TL.
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Affiliation(s)
- Daryl L X Fung
- Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Xu Li
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Carson K Leung
- Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics. mSystems 2022; 7:e0013222. [PMID: 36069455 PMCID: PMC9600536 DOI: 10.1128/msystems.00132-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. However, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, Microbiome Differentiable Interpretable Temporal Rule Engine (MDITRE), which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing data sets, we demonstrate that in almost all cases, MDITRE performs on par with or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through case studies can be used to derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes. IMPORTANCE The human microbiome, or collection of microbes living on and within us, changes over time. Linking these changes to the status of the human host is crucial to understanding how the microbiome influences a variety of human diseases. Due to the large scale and complexity of microbiome data, computational methods are essential. Existing computational methods for linking changes in the microbiome to the status of the human host are either unable to scale to large and complex microbiome data sets or cannot produce human-interpretable outputs. We present a new computational method and software package that overcomes the limitations of previous methods, allowing researchers to analyze larger and more complex data sets while producing easily interpretable outputs. Our method has the potential to enable new insights into how changes in the microbiome over time maintain health or lead to disease in humans and facilitate the development of diagnostic tests based on the microbiome.
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Hernández Medina R, Kutuzova S, Nielsen KN, Johansen J, Hansen LH, Nielsen M, Rasmussen S. Machine learning and deep learning applications in microbiome research. ISME COMMUNICATIONS 2022; 2:98. [PMID: 37938690 PMCID: PMC9723725 DOI: 10.1038/s43705-022-00182-9] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 05/27/2023]
Abstract
The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data - being compositional, sparse, and high-dimensional - necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.
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Affiliation(s)
- Ricardo Hernández Medina
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark
| | - Svetlana Kutuzova
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark
- Department of Computer Science, University of Copenhagen, DK-2100, Copenhagen Ø, Denmark
| | - Knud Nor Nielsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-1871, Frederiksberg, Denmark
| | - Joachim Johansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark
| | - Lars Hestbjerg Hansen
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-1871, Frederiksberg, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, DK-2100, Copenhagen Ø, Denmark.
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark.
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Xie W, Zheng Z, Zhang W, Huang L, Lin Q, Wong KC. SRG-vote: Predicting miRNA-gene relationships via embedding and LSTM ensemble. IEEE J Biomed Health Inform 2022; 26:4335-4344. [PMID: 35471879 DOI: 10.1109/jbhi.2022.3169542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
AbstractTargeted therapy for one for a set of genes has made it possible to apply precision medicine for different patients due to the existence of tumor heterogeneity. However, how to regulate those genes are still problematic. One of the natural regulators of genes is microRNAs. Thus, a better understanding of the miRNA-gene interaction mechanism might contribute to future diagnosis, prevention, and cancer therapy. The interactions between microRNA and genes play an essential role in molecular genetics. The in-vivo experiments validating the relationships between them are time-consuming, money-costly, and labor-intensive. With the development of high-throughput technology, we dealt with tons of biological data. However, extracting features from tremendous raw data and making a mathematical model is still a challenging topic. Machine learning and deep learning algorithms have become powerful tools in dealing with biological data. Inspired by this, in this paper, we propose a model that combines features/embedding extraction methods, deep learning algorithms, and a voting system. We leverage doc2vec to generate sequential embedding from molecular sequences. The role2vec, GCN, and GMM for geometrical embedding were generated from the complex network from similarity and pair-wise datasets. For the deep learning algorithms, we leveraged LSTM and Bi-LSTM according to different embedding and features. Finally, we adopted a voting system to balance results from different data sources. The results have shown that our voting system could achieve a higher AUC than the existing benchmark. The case studies demonstrate that our model could reveal potential relationships between miRNAs and genes. The source code, features, and predictive results can be downloaded at https://github.com/Xshelton/SRG-vote.
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Zha Y, Ning K. Ontology-aware neural network: a general framework for pattern mining from microbiome data. Brief Bioinform 2022; 23:bbac005. [PMID: 35091743 PMCID: PMC8921649 DOI: 10.1093/bib/bbac005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/30/2021] [Accepted: 01/04/2022] [Indexed: 11/23/2022] Open
Abstract
With the rapid accumulation of microbiome data around the world, numerous computational bioinformatics methods have been developed for pattern mining from such paramount microbiome data. Current microbiome data mining methods, such as gene and species mining, rely heavily on sequence comparison. Most of these methods, however, have a clear trade-off, particularly, when it comes to big-data analytical efficiency and accuracy. Microbiome entities are usually organized in ontology structures, and pattern mining methods that have considered ontology structures could offer advantages in mining efficiency and accuracy. Here, we have summarized the ontology-aware neural network (ONN) as a novel framework for microbiome data mining. We have discussed the applications of ONN in multiple contexts, including gene mining, species mining and microbial community dynamic pattern mining. We have then highlighted one of the most important characteristics of ONN, namely, novel knowledge discovery, which makes ONN a standout among all microbiome data mining methods. Finally, we have provided several applications to showcase the advantage of ONN over other methods in microbiome data mining. In summary, ONN represents a paradigm shift for pattern mining from microbiome data: from traditional machine learning approach to ontology-aware and model-based approach, which has found its broad application scenarios in microbiome data mining.
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Affiliation(s)
- Yuguo Zha
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, Center of AI Biology, College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road Wuhan, Hubei, Wuhan 430074, China
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, Center of AI Biology, College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road Wuhan, Hubei, Wuhan 430074, China
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