Classifying COVID-19 Spike Sequences from Geographic Location Using Deep Learning

Ali, Sarwan; Bello, Babatunde Kazeem; Patterson, Murray

doi:10.48550/arxiv.2110.00809

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…Several methods have been proposed recently for the classification of spike sequences. Authors in [33,69] used k-mers along with a kernel-based approach to classify SARS-COV-2 spike sequences. Authors in [32] proposed the use of one-hot encoding to classify the viral hosts of coronaviridae using spike sequences only.…”

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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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

Self Cite

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show abstract

“…Several methods have been proposed recently for the classification of spike sequences. Authors in [31,67] uses k-mers along with kernel based approach to classify the spike sequences. Authors in [30] propose the use of one-hot encoding to classify the viral hosts of coronavirus using spike sequences only.…”

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.

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“…Because of the rapid global spread of COVID-19, and the cooperation of medical institutions worldwide, a tremendous amount of public data -more data than ever before for a single virus -has been made available for researchers [14,9,5]. This "big data" opens up new opportunities to analyse the behavior of this virus [21,20].…”

Section: Introductionmentioning

confidence: 99%

Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models

Ali¹,

Zhou²,

Patterson³

2021

Preprint

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Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before, by leveraging such big data. This data is of many different varieties, and can be of different levels of veracity e.g., precise, imprecise, uncertain, and missing, making it challenging to extract important information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform -often in real-time -the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning based algorithms to this big data is a natural approach to take to this aim, since they can quickly scale to such data, and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19, and for potential future pandemics in general.In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation, and then propose a model that first performs efficient feature selection from such representation. We apply this approach on two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most

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Classifying COVID-19 Spike Sequences from Geographic Location Using Deep Learning

Cited by 3 publications

References 37 publications

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

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

Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models

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