Machine Learning and OLAP on Big COVID-19 Data

Leung, Carson K.; Chen, Yubo; Hoi, Calvin S. H.; Shang, Siyuan; Cuzzocrea, Alfredo

doi:10.1109/bigdata50022.2020.9378407

Cited by 51 publications

(15 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Implementation of Spike2Vec is done in Python and the code is available online for reproducibility 3 . Our pre-processed data is also available online 4 , which can be used after agreeing to terms and conditions of GISAID 5 . For the classification algorithms, we use 1% data for training and 99% for testing.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Very few fields of study remain untouched in the big data era, as massive amounts of data are collected in every domain from finance [1], [2] to astronomy [3], [4]. The field of biomedical and health informatics is no exception; one which has had a recent and rather rapid growth spurt in the amount of available data, due to the COVID-19 pandemic [5], [6]. One facet of this increase is the amount of genomic data becoming available for COVID-19 in databases such as GISAID 1 , where several million viral genome (virome) sequences of COVID-19 -or more precisely, SARS-CoV-2 2 -are available.…”

Section: Introductionmentioning

confidence: 99%

“…https://github.com/sarwanpasha/Spike2Vec 4 https://drive.google.com/drive/folders/1-YmIM8ipFpj-glr9hSF3t6VuofrpgWUa? usp=sharing5 https://www.gisaid.org/…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

Ali¹,

Patterson²

2021

Preprint

View full text Add to dashboard Cite

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. As an example of this, we 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 to the ones reported by the USA based Centers for Disease Control and Prevention (CDC) for different variants.

show abstract

Section: A Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ali¹,

Patterson²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Very few fields of study remain untouched in the big data era, as massive amounts of data are collected in every domain from finance [1], [2] to astronomy [3], [4]. The field of biomedical and health informatics is no exception; one which has had a recent and rather rapid growth spurt in the amount of available data, due to the COVID-19 pandemic [5]- [8]. One facet of this increase is the amount of genomic data becoming available for COVID-19 in databases such as GISAID [9], where several million viral genome (virome) sequences of COVID-19 -or more precisely, SARS-CoV-2 1 -are available.…”

Section: Introductionmentioning

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)

View full text Add to dashboard Cite

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.

show abstract

“…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]. Despite these opportunities, the huge size of the data poses a challenge for its processing on smaller systems [9].…”

Section: Introductionmentioning

confidence: 99%

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

Ali¹,

Zhou²,

Patterson³

2021

Preprint

View full text Add to dashboard Cite

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

show abstract

Machine Learning and OLAP on Big COVID-19 Data

Cited by 51 publications

References 46 publications

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

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

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

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

Contact Info

Product

Resources

About