首页|Imperial College London Reports Findings in COVID-19 (Assessment and classificat ion of COVID-19 DNA sequence using pairwise features concatenation from Multi-Tr ansformer and deep features with Machine Learning models)
Imperial College London Reports Findings in COVID-19 (Assessment and classificat ion of COVID-19 DNA sequence using pairwise features concatenation from Multi-Tr ansformer and deep features with Machine Learning models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Coronavirus - COVID-19 is the subject of a report. According to news reporting from London, United Kin gdom, by NewsRx journalists, research stated, “The 2019 novel coronavirus (renam ed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 18 4 countries with over 1.5 million confirmed cases. Such a major viral outbreak d emands early elucidation of taxonomic classification and origin of the virus gen omic sequence, for strategic planning, containment, and treatment.” The news correspondents obtained a quote from the research from Imperial College London, “The emerging global infectious COVID-19 disease by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) presents critical threats to glo bal public health and the economy since it was identified in late December 2019 in China. The virus has gone through various pathways of evolution. Due to the c ontinued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying deep le arning and machine learning approaches. In a general computational context for b iomedical data analysis, DNA sequence classification is a crucial challenge. Sev eral machine and deep learning techniques have been used in recent years to comp lete this task with some success. The classification of DNA sequences is a key r esearch area in bioinformatics as it enables researchers to conduct genomic anal ysis and detect possible diseases. In this paper, three state-of-the-art deep le arning-based models are proposed using two DNA sequence conversion methods. We a lso proposed a novel multi-transformer deep learning model and pairwise features fusion technique for DNA sequence classification. Furthermore, deep features ar e extracted from the last layer of the multi-transformer and used in machine-lea rning models for DNA sequence classification. The k-mer and one-hot encoding seq uence conversion techniques have been presented. The proposed multi-transformer achieved the highest performance in COVID DNA sequence classification. Automatic identification and classification of viruses are essential to avoid an outbreak like COVID-19.”
LondonUnited KingdomEuropeCOVID-19CoronavirusCyborgsDNA ResearchDNA Sequence ProteomicsDeep LearningDe oxyribonucleic AcidEmerging TechnologiesGeneticsHealth and MedicineMachi ne LearningRNA VirusesRespiratory Tract Diseases and ConditionsRisk and Pr eventionSARSSARS-CoV-2Severe Acute Respiratory SyndromeSevere Acute Resp iratory Syndrome Coronavirus 2ViralVirology
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