PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses

Mateos, Pablo Acera; Balboa, Renzo F.; Easteal, Simon; Eyras, Eduardo; Patel, Hardip R.

doi:10.1038/s41598-021-82043-4

Cited by 18 publications

(12 citation statements)

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“…The classification of SARS-CoV-2 and other co-infecting RNA viruses is a challenging problem. We used the data provided by the authors of the paper titled ‘PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses’ [ 14 ]. The dataset consists of genome sequences corresponding to SARS-CoV-2 (class-0), Coronaviridae (class-1), Metapneumovirus (class-2), Rhinovirus (class-3) and Influenza (class-4).…”

Section: Dataset Detailsmentioning

confidence: 99%

“…The dataset details are provided in Table 2 . The authors [ 14 ] have made the data publicly available. 2 A five class classification problem is formulated with this dataset.…”

Section: Dataset Detailsmentioning

confidence: 99%

“…Furthermore, the DL model is used for specific primer design. Authors in [ 14 ] use a DL model for the classification of SARS-COV-2 viral genome sequence from co-infecting RNA sequences (Coronaviridae, Metapneumovirus, Rhinovirus, Influenza). In [ 15 , 16 ], classical machine learning techniques were used for the classification of SARS-CoV-2 genome sequence.…”

Section: Introductionmentioning

confidence: 99%

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Classification of SARS-CoV-2 viral genome sequences using Neurochaos Learning

Harikrishnan

Pranay

Nagaraj

2022

Med Biol Eng Comput

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The high spread rate of SARS-CoV-2 virus has put the researchers all over the world in a demanding situation. The need of the hour is to develop novel learning algorithms that can effectively learn a general pattern by training with fewer genome sequences of coronavirus. Learning from very few training samples is necessary and important during the beginning of a disease outbreak when sequencing data is limited. This is because a successful detection and isolation of patients can curb the spread of the virus. However, this poses a huge challenge for machine learning and deep learning algorithms as they require huge amounts of training data to learn the pattern and distinguish from other closely related viruses. In this paper, we propose a new paradigm – Neurochaos Learning (NL) for classification of coronavirus genome sequence that addresses this specific problem. NL is inspired from the empirical evidence of chaos and non-linearity at the level of neurons in biological neural networks. The average sensitivity, specificity and accuracy for NL are 0.998, 0.999 and 0.998 respectively for the multiclass classification problem (SARS-CoV-2, Coronaviridae, Metapneumovirus, Rhinovirus and Influenza) using leave one out crossvalidation. With just one training sample per class for 1000 independent random trials of training, we report an average macro F1-score for the classification of SARS-CoV-2 from SARS-CoV-1 genome sequences. We compare the performance of NL with K-nearest neighbours (KNN), logistic regression, random forest, SVM, and naïve Bayes classifiers. We foresee promising future applications in genome classification using NL with novel combinations of chaotic feature engineering and other machine learning algorithms. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11517-022-02591-3.

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Section: Dataset Detailsmentioning

confidence: 99%

“…The dataset details are provided in Table 2 . The authors [ 14 ] have made the data publicly available. 2 A five class classification problem is formulated with this dataset.…”

Section: Dataset Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of SARS-CoV-2 viral genome sequences using Neurochaos Learning

Harikrishnan

Pranay

Nagaraj

2022

Med Biol Eng Comput

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“…Mateos et al [34] presented a deep learning technique for the classification of SARS-CoV-2 and co-infecting RNA viruses. In [35], authors used an convolutional neural network for classification and accurate detection of SARS-CoV-2.…”

Section: Introductionmentioning

confidence: 99%

Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses

Ahmed

Jeon

2021

Interdiscip Sci Comput Life Sci

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Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population's health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus's origin, behavior, and structure, which might help produce/develop vaccines, antiviral drugs, and efficient preventive strategies. This paper introduces an artificial intelligence based system to perform genome sequence analysis of COVID-19 and alike viruses, e.g., SARS, middle east respiratory syndrome, and Ebola. The system helps to get important information from the genome sequences of different viruses. We perform comparative data analysis by extracting basic information of COVID-19 and other genome sequences, including information of nucleotides composition and their frequency, tri-nucleotide compositions, count of amino acids, alignment between genome sequences, and their DNA similarity information. We use different visualization methods to analyze these viruses' genome sequences and, finally, apply machine learning based classifier support vector machine to classify different genome sequences. The data set of different virus genome sequences are obtained from an online publicly accessible data center repository. The system achieves good classification results with an accuracy of 97% for COVID-19, 96%, SARS, and 95% for MERS and Ebola genome sequences, respectively.

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“…Together with the other efforts applying novel ML methodology to pathogen genomics, 9,10 lion's share of SARS-CoV-2 surveillance sequencing is performed by precious few nations and laboratories, making our datasets shortsighted at best. Yet, should the current trends in pathogen sequence data collection continue, perhaps in a future powered by a handful of big-data resources like Global Initiative on Sharing Avian Influenza Data (GISAID), 2 ML may drive the new era of infection biology and change our approach to emerging pathogens from reactive to proactive.…”

mentioning

confidence: 99%

Sequencing meets machine learning to fight emerging pathogens: A preview

Yakimovich

2022

Patterns

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PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses

Cited by 18 publications

References 50 publications

Classification of SARS-CoV-2 viral genome sequences using Neurochaos Learning

Classification of SARS-CoV-2 viral genome sequences using Neurochaos Learning

Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses

Sequencing meets machine learning to fight emerging pathogens: A preview

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