Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants

Nan, Bei-Guang; Zhang, Sen; Li, Yuchang; Kang, Xiaoping; Chen, Yuehong; Li, Lin; Jiang, Tao; Li, Jing

doi:10.3390/v14051072

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…Adaptation phenotypes of viruses to bats and other mammals are supported by parallel viral genotypes. A coarse-grained representation of the viral genome as compositional traits, such as DNT and DCR, is hostspecific and predictable with machine learning or deep learning approaches for CoVs (Pollock et al, 2020;Li et al, 2022;Nan et al, 2022), influenza viruses (Taubenberger and Kash, 2010;Li et al, 2020), and other viruses (Bahir et al, 2009;Babayan et al, 2018;Chen et al, 2020). Fine-tuned sequential representation has been indicated to be sensitive to predicting the adaptation of SARS-CoV-2 Omicron sublineages with deep learning (Nan et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Traditional phylogenetic analysis can sufficiently evaluate the cross-species infection risk or any bat CoV (Lima et al, 2013;Seyran et al, 2021). More recently, machine learning or deep learning approaches based on big sequencing data have led to remarkable predictions of the host adaptation (Li et al, 2022;Nan et al, 2022), evolution (Hie et al, 2021), transmissibility (Fischhoff et al, 2021), virus-host interaction (Dey et al, 2020), and pathogenicity (Gussow et al, 2020) of SARS-CoV-2 and other viruses (Li et al, 2020). Host-specific compositional features in the virus genome have been indicated by the representation traits, such as dinucleotides (DNTs) (Li et al, 2020), DNT composition representation (DCR) (Li et al, 2022), and Uniform Manifold Approximation and Projection (UMAP) (Hie et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning

Tian

Zhang

et al. 2023

Front. Microbiol.

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IntroductionCoronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals.MethodsThe CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, ORF1ab and Spike. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs.Results and discussionThe results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation.ConclusionGenomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning

Tian

Zhang

et al. 2023

Front. Microbiol.

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“…Considering the time it would take for these inventions to make an impact in a relatively distant field like archiving, it appeared rational to start the study in 2015. A superficial review of journals before this date was adequate to authorize our conclusion; additionally, we desired to concentrate on current controversies and future perceptions and hence considered using modern AI [ 27 , 37 ]. This research work focuses on avoiding the existing drawbacks of CNN-RNN techniques [ 38 , 39 ], which are also listed in Table 2 .…”

Section: System Methodologymentioning

confidence: 99%

“…Table 2 lists and delivers more precise outcomes than molded feature-based prototypes [ 5 , 9 , 17 , 26 ]. A deep RNN-CNN [ 17 , 18 , 22 , 27 ] framework, COVIDNet-CT [ 12 ], RestNet [ 27 ], ERNN [ 17 , 28 ], and CNN [ 3 ] were established to detect and predict Omicron infection from chest CT-scan images. In [ 1 ], the authors proposed a CNN and LSTM-based DL technique to diagnose COVID-19 instinctively through the X-ray image dataset.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

Gupta

Srinivasulu

Hiran

et al. 2022

Interdisciplinary Perspectives on Infectious Diseases

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COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.

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“…Convolutional neural networks (CNNs) are widely used in the field of image recognition; however, in recent years, CNN has also performed well in predicting the adaptability of virus hosts. A CNN predictor based on the dinucleotide composition representation (DCR) [ 54 ] and AA [ 55 ] could provide real-time predictions of emerging SARS-CoV-2 variants. Thus, AI methods are expected to learn the adaptation of swine CoVs to other mammals, and to assess the possible intermediate host role of pigs for coronaviruses.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor

Jiang

Zhang

Kang

et al. 2023

Viruses

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Swine coronaviruses (CoVs) have been found to cause infection in humans, suggesting that Suiformes might be potential intermediate hosts in CoV transmission from their natural hosts to humans. The present study aims to establish convolutional neural network (CNN) models to predict host adaptation of swine CoVs. Decomposing of each ORF1ab and Spike sequence was performed with dinucleotide composition representation (DCR) and other traits. The relationship between CoVs from different adaptive hosts was analyzed by unsupervised learning, and CNN models based on DCR of ORF1ab and Spike were built to predict the host adaptation of swine CoVs. The rationality of the models was verified with phylogenetic analysis. Unsupervised learning showed that there is a multiple host adaptation of different swine CoVs. According to the adaptation prediction of CNN models, swine acute diarrhea syndrome CoV (SADS-CoV) and porcine epidemic diarrhea virus (PEDV) are adapted to Chiroptera, swine transmissible gastroenteritis virus (TGEV) is adapted to Carnivora, porcine hemagglutinating encephalomyelitis (PHEV) might be adapted to Primate, Rodent, and Lagomorpha, and porcine deltacoronavirus (PDCoV) might be adapted to Chiroptera, Artiodactyla, and Carnivora. In summary, the DCR trait has been confirmed to be representative for the CoV genome, and the DCR-based deep learning model works well to assess the adaptation of swine CoVs to other mammals. Suiformes might be intermediate hosts for human CoVs and other mammalian CoVs. The present study provides a novel approach to assess the risk of adaptation and transmission to humans and other mammals of swine CoVs.

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Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants

Cited by 6 publications

References 37 publications

Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning

Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning

Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor

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