Predicting microbiomes through a deep latent space

García-Jiménez, Beatriz; Muñoz, J. A.; Cabello, Sara; Medina, Joaquı́n; Wilkinson, Mark D.

doi:10.1101/2020.04.27.063974

Cited by 7 publications

(8 citation statements)

References 45 publications

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“…Deep learning techniques are actively applied in microbiome research [50–58], such as for classifying samples that shifted to a diseased state [59], predicting infection complications in immunocompromised patients [60], or predicting the temporal or spatial evolution of certain species collection [61,62]. However, to the best of our knowledge, the potential of deep learning for predicting the effect of changing species assemblages was not explored nor validated before.…”

Section: Discussionmentioning

confidence: 99%

Predicting microbiome compositions from species assemblages through deep learning

Michel‐Mata

Wang

Liu

et al. 2022

iMeta

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Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' wellbeing. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowing any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply our framework to data from in vitro and in vivo microbial communities, including ocean and soil microbiota, Drosophila melanogaster gut microbiota, and human gut and oral microbiota. We find that our framework learns to perform accurate out‐of‐sample predictions of complex community compositions from a small number of training samples. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

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Section: Discussionmentioning

confidence: 99%

Predicting microbiome compositions from species assemblages through deep learning

Michel‐Mata

Wang

Liu

et al. 2022

iMeta

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“…Deep learning techniques are actively applied to microbiome research [41][42][43][44][45][46][47][48][49] such as for classifying samples that shifted to a diseased state 50 , predicting infection complications in immunocompromised patients 51 , or predicting the temporal or spatial evolution of certain species collection 52,53 . However, to the best of our knowledge, the potential of deep learning for predicting the effect of changing species collection was not explored nor validated before.…”

Section: Discussionmentioning

confidence: 99%

Predicting microbiome compositions from species assemblages through deep learning

Michel‐Mata

Wang

Liu

et al. 2021

Preprint

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Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment1,2 or the well-being of their hosts3-6. Successfully managing these microbial communities requires the ability to predict the community composition based on the species assemblage7. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical8, biochemical9, and ecological10,11 processes governing the microbial dynamics. To overcome this challenge, here we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply it to experimental data of both in vitro and in vivo communities, including ocean and soil microbial communities12,13, Drosophila melanogaster gut microbiota14, and human gut and oral microbiota15. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

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“…Nonetheless, previous research has demonstrated improved predictive power can be attained by marrying different data modalities, such as microbiome, genetic, and environmental data [92]. For instance, García-Jiménez et al [93] implemented a concept of multimodal embedding by minimizing the distance between the two latent spaces created by the separate encoders of two modalities (environmental variables and microbial composition). A lineage of work on multimodal variational autoencoders investigates the most suitable way of combining the latent spaces of individual modalities depending on the dataset properties [94][95][96][97][98][99].…”

Section: Novel Techniques To Keep On the Watchlistmentioning

confidence: 99%

Machine learning and deep learning applications in microbiome research

Medina

Kutuzova

Nielsen

et al. 2022

ISME Communications

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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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Predicting microbiomes through a deep latent space

Abstract: Motivation: Microbial communities influence their environment by modifying the availability of compounds such as nutrients or chemical elicitors.

Cited by 7 publications

References 45 publications

Predicting microbiome compositions from species assemblages through deep learning

Predicting microbiome compositions from species assemblages through deep learning

Predicting microbiome compositions from species assemblages through deep learning

Machine learning and deep learning applications in microbiome research

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