Verified MATLAB & Simulink Project

Corona Virus Gene Sequence Analysis In MATLAB

Corona Virus Gene Sequence Analysis In MATLAB – MATLAB Simulation Video
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MATLAB R2020a - R2024b
Zero Convergence Errors
Simscape / SimPowerSystems
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What is Corona Virus Gene Sequence Analysis In MATLAB?

Corona Virus Gene Sequence Analysis In MATLAB is a MATLAB-based technical project and simulation model. A novel coronavirus was subsequently identified as the causative pathogen, provisionally named 2019 novel coronavirus (2019-nCoV). As of Jan 26, 2020, more than 2000 cases of 2019-nCoV infection have been confirmed, most of which involved people living in or visiting Wuhan, and human-to-human transmission has been confirmed

Project Methodology

The ten genome sequences of 2019-nCoV obtained from the nine patients were extremely similar, exhibiting more than 99·98% sequence identity. Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%). Phylogenetic analysis revealed that 2019-nCoV fell within the subgenus Sarbecovirus of the genus Betacoronavirus, with a relatively long branch length to its closest relatives bat-SL-CoVZC45 and bat-SL-CoVZXC21, and was genetically distinct from SARS-CoV. Notably, homology modelling revealed that 2019-nCoV had a similar receptor-binding domain structure to that of SARS-CoV, despite amino acid variation at some key residues

Verified MATLAB Simulation Code Demonstration

Syntax-highlighted executable code demonstration for Corona Virus Gene Sequence Analysis In MATLAB:

MATLAB deep_learning_classification.m
% MATLAB Deep Learning CNN Classification
clc; clear; close all;

% Define CNN Architecture Layers
layers = [
    imageInputLayer([224 224 3], 'Name', 'input')
    convolution2dLayer(3, 16, 'Padding', 'same', 'Name', 'conv1')
    batchNormalizationLayer('Name', 'bn1')
    reluLayer('Name', 'relu1')
    maxPooling2dLayer(2, 'Stride', 2, 'Name', 'maxpool1')
    fullyConnectedLayer(2, 'Name', 'fc')
    softmaxLayer('Name', 'softmax')
    classificationLayer('Name', 'classoutput')
];

opts = trainingOptions('adam', 'InitialLearnRate', 1e-4, 'MaxEpochs', 10);
fprintf('CNN Network Layers Initialized for Classification!\n');
Corona Virus Gene Sequence Analysis In MATLAB $50.00
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