Towards end-to-end disease prediction from raw metagenomic data

Queyrel, Maxence; Prifti, Edi; Templier, Alexadnre; Zucker, Jean-Daniel

doi:10.1101/2020.10.29.360297

Cited by 9 publications

(20 citation statements)

References 40 publications

(68 reference statements)

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“…It aggregates these k-mer embeddings to get read embeddings. Using the same idea, Metagenome2Vec ( [100]) avoids the solution of simply aggregating data, which would lead to losing precision, by using fastDNA ( [99]).…”

Section: Sequence-based Approachesmentioning

confidence: 99%

“…Metagenome2Vec ( [100]), IDMIL ( [132]) and the method described in [130]) use a particular DL paradigm called Multiple Instance Learning. Multiple Instance Learning (MIL) is a supervised learning paradigm that consists of learning from labeled sets of instances, known as 'bags', instead of learning from individually labeled instances.…”

Section: Sequence-based Approachesmentioning

confidence: 99%

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Deep learning methods in metagenomics: a review

Roy

Prifti

Zucker

2023

Preprint

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The ever decreasing cost of sequencing and the multiplication of potential applications for the study of metagenomes have led to an unprecedented increase in the volume of data generated. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome has been shown to play an important role in human health, providing critical information for patient diagnosis and prognosis. However, the analysis of metagenomic data remains challenging for many reasons, including reference catalogs, sparsity and compositionality of the data, to name a few. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. In fact, DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification, and disease prediction. Beyond the generation of predictive models, a key aspect of such methods remains their interpretability. In this article, we provide a systematic review of deep learning approaches in metagenomics, whether based on convolutional networks, autoencoders, or attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the key role the microbiome plays in our health.

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Section: Sequence-based Approachesmentioning

confidence: 99%

Section: Sequence-based Approachesmentioning

confidence: 99%

Deep learning methods in metagenomics: a review

Roy

Prifti

Zucker

2023

Preprint

Self Cite

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“…Several machine learning approaches based on k-mers have been proposed in the literature for classification and clustering tasks [33,35,38,39]. More specifically, there are many classical algorithms for sequence classification [40,41].…”

Section: Related Workmentioning

confidence: 99%

PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences

Ali

Bello

Chourasia

et al. 2022

Biology

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The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models.

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“…Several machine learning approaches based on k-mers have been proposed in the literature for classification and clustering tasks [31,33,36,37]. More specifically, there are a lot of classical algorithms for sequence classification [38,39].…”

Section: Related Workmentioning

confidence: 99%

PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences

Ali¹,

Bello²,

Chourasia³

et al. 2022

Preprint

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The origin of SARS-CoV-2 in humans, which led to the COVID-19 pandemic, is still unknown and is an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona-) viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is an important part of determining host specificity since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among avians, bats, camels, swines, humans and weasels, to name a few. We propose a feature embedding based on the well-known position-weight matrix (PWM), which we call PWM2Vec, and use to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications such as determining protein function, or identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs in the context of host classification from viral sequences to generate a fixed-length feature vector representation. The results on the real world data show that in using PWM2Vec, the machine learning classifiers are able to perform comparably well as the baseline models in terms of predictive performance and runtime -in some cases the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the baselines.

show abstract

Towards end-to-end disease prediction from raw metagenomic data

Cited by 9 publications

References 40 publications

Deep learning methods in metagenomics: a review

Deep learning methods in metagenomics: a review

PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences

PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences

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