Predicting Attributes of Nodes Using Network Structure

Ali, Sarwan; Shakeel, Muhammad Haroon; Khan, Imdadullah; Faizullah, Safiullah; Khan, Muhammad Asad

doi:10.1145/3442390

Cited by 41 publications

(18 citation statements)

References 34 publications

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“…Designing efficient feature vector based representations haave been studied in many domains such as graph analytics [59,60], smart grid [61,62], electromyography (EMG) [63], clinical data analysis [64], network security [65], and text classification [66]. After the spread of COVID-19, efforts have been made to study the behavior of the virus using machine learning approaches.…”

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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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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Existing work on fixed length numerical representation of the data successfully perform different data analytics tasks. It has applications in different domains such as graphs [36], [37], nodes in graphs [38], [39], and electricity consumption [33], [40]. This vector-based representation also achieves significant success in sequence analysis, such as texts [41]- [43], electroencephalography and electromyography sequences [44], [45], networks [46], and biological sequences [32], [47].…”

Section: Literature Reviewmentioning

confidence: 99%

Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences

Ali

2021

2021 IEEE International Conference on Big Data (Big Data)

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With the rapid global spread of COVID-19, more and more data related to this virus is becoming available, including genomic sequence data. The total number of genomic sequences that are publicly available on platforms such as GISAID is currently several million, and is increasing with every day. The availability of such Big Data creates a new opportunity for researchers to study this virus in detail. This is particularly important with all of the dynamics of the COVID-19 variants which emerge and circulate. This rich data source will give us insights on the best ways to perform genomic surveillance for this and future pandemic threats, with the ultimate goal of mitigating or eliminating such threats. Analyzing and processing the several million genomic sequences is a challenging task. Although traditional methods for sequence classification are proven to be effective, they are not designed to deal with these specific types of genomic sequences. Moreover, most of the existing methods also face the issue of scalability. Previous studies which were tailored to coronavirus genomic data proposed to use spike sequences (corresponding to a subsequence of the genome), rather than using the complete genomic sequence, to perform different machine learning (ML) tasks such as classification and clustering. However, those methods suffer from scalability issues.In this paper, we propose an approach called Spike2Vec, an efficient and scalable feature vector representation for each spike sequence that can be used for downstream ML tasks. Through experiments, we show that Spike2Vec is not only scalable on several million spike sequences, but also outperforms the baseline models in terms of prediction accuracy, F1 score, etc. Since this type of study on such huge numbers of spike sequences has not been done before (to the best of our knowledge), we believe that it will open new doors for researchers to use this data and perform different tasks to unfold new information that was not available before. We also use information gain (IG) to compute the importance of each amino acid in the spike sequence. The amino acids with higher IG values tend to be the same as many reported by the USA based Centers for Disease Control and Prevention (CDC) for different variants.

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“…Since the dimensionality of data are another problem while dealing with larger sized sequences, using approximate methods to compute the similarity between two sequences is a popular approach [21,27,28]. The fixed-length numerical embedding methods have been successfully used in literature for other applications such as predicting missing values in graphs [29], text analytics [30][31][32], biology [21,27,33], graph analytics [34,35], classification of electroencephalography and electromyography sequences [36,37], detecting security attacks in networks [38], and electricity consumption in smart grids [39]. The conditional dependencies between variables is also important to study so that their importance can be analyzed in detail [40].…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

2021

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The widespread availability of large amounts of genomic data on the SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an opportunity for researchers to analyze the disease at a level of detail, unlike any virus before it. On the one hand, this will help biologists, policymakers, and other authorities to make timely and appropriate decisions to control the spread of the coronavirus. On the other hand, such studies will help to more effectively deal with any possible future pandemic. Since the SARS-CoV-2 virus contains different variants, each of them having different mutations, performing any analysis on such data becomes a difficult task, given the size of the data. It is well known that much of the variation in the SARS-CoV-2 genome happens disproportionately in the spike region of the genome sequence—the relatively short region which codes for the spike protein(s). In this paper, we propose a robust feature-vector representation of biological sequences that, when combined with the appropriate feature selection method, allows different downstream clustering approaches to perform well on a variety of different measures. We use such proposed approach with an array of clustering techniques to cluster spike protein sequences in order to study the behavior of different known variants that are increasing at a very high rate throughout the world. We use a k-mers based approach first to generate a fixed-length feature vector representation of the spike sequences. We then show that we can efficiently and effectively cluster the spike sequences based on the different variants with the appropriate feature selection. Using a publicly available set of SARS-CoV-2 spike sequences, we perform clustering of these sequences using both hard and soft clustering methods and show that, with our feature selection methods, we can achieve higher F1 scores for the clusters and also better clustering quality metrics compared to baselines.

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Predicting Attributes of Nodes Using Network Structure

Cited by 41 publications

References 34 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

Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences

Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

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