Convolutional Neural Network Applied to SARS|CoV|2 Sequence ...

Convolutional Neural Network Applied to SARS-CoV-2 Sequence ...

This work proposes a high-accuracy technique to classify viruses and other organisms from a genome sequence using a deep learning convolutional neural network ...

Enhanced Deep Convolutional Neural Network for SARS-CoV-2 ...

Alternative robust methods such as machine learning are currently employed in genome sequence analysis and classification, and it can be applied ...

Convolutional Neural Network Applied to SARS-CoV-2 Sequence ...

PDF | COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the Coronaviridade ...

Exploring coronavirus sequence motifs through convolutional neural ...

As a result, a DL algorithm CNN is used to categorize SARS-CoV-2 genes and other Coronavirus genes. 3.4. Proposed architecture. In the proposed ...

Assessment and classification of COVID-19 DNA sequence using ...

The raw DNA sequences cannot be used as a feature in the convolutional neural network (CNN) based deep learning models. We must convert character DNA sequences ...

Automated COVID-19 detection with convolutional neural networks

The COVIDx dataset was used to train the DNN architectures, and before training, the chest CXR images underwent enhancement by removing embedded ...

Deep Learning for SARS COV-2 Genome Sequences - PMC

This numerical matrix is the input that is subsequently fed into a CNN. Additionally, one-hot vectors that are used in this paper to represent SARS CoV-2 gene ...

Predicting SARS-CoV-2 infection among hemodialysis patients ...

For example, CNN and LSTM have been used to analyze chest radiographs (CXR) and computed tomography (CT) images to detect COVID-19 cases.

Convolutional Neural Network Applied to SARS-CoV-2 Sequence ...

Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification. Câmara, Gabriel B M; Coutinho, Maria G F; Silva, Lucileide M D da; Gadelha, Walter V ...

New proposal of viral genome representation applied in the ...

Convolutional Neural Network architecture used for classification of SARS-CoV-2 ... DNA sequence classification by convolutional neural network.

Convolutional Neural Networks Based on Sequential Spike Predict ...

More than 9,000,000 full genome sequences of SARS-CoV-2 viruses were ... Random sampling was used to obtain the sequences in the ...

Drug discovery through Covid-19 genome sequencing with siamese ...

The work [43] used algebraic topology with the deep convolutional neural network (DCNN) to discover the protease inhibitor. For this purpose ...

Next Generation Sequence Prediction Intelligent System for SARS ...

Next Generation Sequence Prediction Intelligent System for SARS-COV-2 Using Deep Learning Neural Network. Abstract: Viral mutations can occur that prevent ...

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS ...

(2020) have used deep neural networks with X-ray images for ... deep learning) which is capable of learning order dependence in sequence ...

Obtaining an accurate estimate of the Covid-19 mutation rate via ...

... sequence analysis preeminent themes using convolutional neural networks ... used to determine if a sequence is SARS-CoV2 or not. In the second step a ...

Analysis of DNA Sequence Classification Using CNN and Hybrid ...

The complete DNA/Genomic sequence of the viruses like COVID, SARS ... The bidirectional LSTM + CNN hybrid model is used for DNA sequence ...

Convolutional Neural Network Applied to SARS-CoV-2 Sequence ...

Thus, this work proposes a high-accuracy technique to classify viruses and other organisms from a genome sequence using a deep learning convolutional neural ...

DNA Sequence Classification by Convolutional Neural Network

The model contains 2 convolutional layers. Each of these layers is followed by a sub-sampling layer. These layers are used to extract features ...

Enhanced Deep Convolutional Neural Network for SARS-CoV-2 ...

Alternative robust methods such as machine learning are currently employed in genome sequence analysis and classification, and it can be applied in classifying ...

Convolutional neural network architectures for predicting DNA ...

Instead of processing 2-D images with three color channels (R,G,B), we consider a genome sequence as a fixed length 1-D sequence window with ...