Applying Deep Learning for Genome Detection of Coronavirus

Rani, Geeta; Oza, Meet Ganpatlal; Dhaka, Vijaypal Singh; Pradhan, Nitesh; Verma, Sahil; Rodrigues, Joel J. P. C.

doi:10.21203/rs.3.rs-93564/v1

Cited by 4 publications

(5 citation statements)

References 20 publications

(72 reference statements)

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“…Therefore, it provides little insight into drug discovery. However, the accuracy of the deep learning model presented in [29] was greater than that of the model proposed in [27,28] using the stacked sparse autoencoder approach, and the image representation of the whole genome sequence [31] calculated the similarity score between the genome of "SARS-CoV-2" and the genomes of other viruses, including SARS-CoV, MERS-CoV, HIV, and HTLV. Working on the CNN-and LSTMbased "genome similarity predictor" model, which is used to classify genomes and predict the "SARS-CoV2" and other viruses' "genomic similarity score.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Sherif

Ahmed

2022

J. Eng. Appl. Sci.

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SARS-CoV-2’s population structure might have a substantial impact on public health management and diagnostics if it can be identified. It is critical to rapidly monitor and characterize their lineages circulating globally for a more accurate diagnosis, improved care, and faster treatment. For a clearer picture of the SARS-CoV-2 population structure, clustering the sequencing data is essential. Here, deep clustering techniques were used to automatically group 29,017 different strains of SARS-CoV-2 into clusters. We aim to identify the main clusters of SARS-CoV-2 population structure based on convolutional autoencoder (CAE) trained with numerical feature vectors mapped from coronavirus Spike peptide sequences. Our clustering findings revealed that there are six large SARS-CoV-2 population clusters (C1, C2, C3, C4, C5, C6). These clusters contained 43 unique lineages in which the 29,017 publicly accessible strains were dispersed. In all the resulting six clusters, the genetic distances within the same cluster (intra-cluster distances) are less than the distances between inter-clusters (P-value 0.0019, Wilcoxon rank-sum test). This indicates substantial evidence of a connection between the cluster’s lineages. Furthermore, comparisons of the K-means and hierarchical clustering methods have been examined against the proposed deep learning clustering method. The intra-cluster genetic distances of the proposed method were smaller than those of K-means alone and hierarchical clustering methods. We used T-distributed stochastic-neighbor embedding (t-SNE) to show the outcomes of the deep learning clustering. The strains were isolated correctly between clusters in the t-SNE plot. Our results showed that the (C5) cluster exclusively includes Gamma lineage (P.1) only, suggesting that strains of P.1 in C5 are more diversified than those in the other clusters. Our study indicates that the genetic similarity between strains in the same cluster enables a better understanding of the major features of the unknown population lineages when compared to some of the more prevalent viral isolates. This information helps researchers figure out how the virus changed over time and spread to people all over the world.

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Section: Discussionmentioning

confidence: 99%

“…Rani et.al. [31] calculated the similarity score between the genome of "SARS-CoV-2" and the genomes of other viruses, including SARS-CoV, MERS-CoV, HIV, and HTLV. Working on the CNN-and LSTM-based "genome similarity predictor" model, which is used to classify genomes and predict the "SARS-CoV2" and other viruses' "genomic similarity score.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Sherif

Ahmed

2022

J. Eng. Appl. Sci.

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“…Some studies also focused on developing machine learning models by finding the degree of similarity of a COVID-19 genome against a given genomic sequence [8,[25][26][27]. Rani et al [8] developed a deep learning model to find similarities between a given genome and a COVID-19 genome and detecting the presence of COVID-19 in humans using data from the National Centre for Biotechnology Information. The authors employed Convolutional Neural Networks (CNN) and Long-Short-Term-Memory (LSTM) for improving the accuracy of classification and similarity score prediction.…”

Section: Review Of Relevant Studiesmentioning

confidence: 99%

“…The apparent lag in getting test results prolongs the time in which an asymptomatic person may inadvertently transmit the disease to others. Rani et al [8] also argue that doctors spend time studying COVID-19 test reports which can be time-consuming. In addition, molecular-based methods such as rRT-PCR and microarrays are time consuming and can lead to contamination of sequences [9].…”

Section: Introductionmentioning

confidence: 99%

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach

Adjuik

Ananey-Obiri

2022

Int. j. inf. tecnol.

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The world was ambushed in 2019 by the COVID-19 virus which affected the health, economy, and lifestyle of individuals worldwide. One way of combating such a public health concern is by using appropriate, rapid, and unbiased diagnostic tools for quick detection of infected people. However, a current dearth of bioinformatics tools necessitates modeling studies to help diagnose COVID-19 cases. Molecular-based methods such as the real-time reverse transcription polymerase chain reaction (rRT-PCR) for detecting COVID-19 is time consuming and prone to contamination. Modern bioinformatics tools have made it possible to create large databases of protein sequences of various diseases, apply data mining techniques, and accurately diagnose diseases. However, the current sequence alignment tools that use these databases are not able to detect novel COVID-19 viral sequences due to high sequence dissimilarity. The objective of this study, therefore, was to develop models that can accurately classify COVID-19 viral sequences rapidly using protein vectors generated by neural word embedding technique. Five machine learning models; K nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), Linear discriminant analysis (LDA), and Logistic regression were developed using datasets from the National Center for Biotechnology. Our results suggest, the RF model performed better than all other models on the training dataset with 99% accuracy score and 99.5% accuracy on the testing dataset. The implication of this study is that, rapid detection of the COVID-19 virus in suspected cases could potentially save lives as less time will be needed to ascertain the status of a patient.

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“…Context-aware and interpretable machine learning (ML) and deep learning (DL) have gained remarkable attention in human health monitoring [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], crop health monitoring, and yield prediction [ 17 ]. These models are effective to give automatic, accurate, and quick systems for plant disease detection and classification.…”

Section: Introductionmentioning

confidence: 99%

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Kundu

Rani

Dhaka

et al. 2021

Sensors

Self Cite

140

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Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

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Applying Deep Learning for Genome Detection of Coronavirus

Cited by 4 publications

References 20 publications

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

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