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)
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-冠状病毒的新研究-COVID-19是一篇报道的主题。根据NewsRx记者从伦敦联合王国发回的新闻报道，研究表明，“2019年新型冠状病毒（Renam ed SARS-CoV-2，通常称为COVID-19病毒）已经蔓延到18 4个国家，确诊病例超过150万例。这样一次重大的病毒爆发需要及早阐明病毒基因组序列的分类分类和来源，以便战略规划、遏制和治疗。”新闻记者从伦敦帝国理工学院的研究中获得了一句话，“新型严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引起的全球传染性COVID-19疾病自2019年12月底在中国被发现以来，对全球公共卫生和经济构成了严重威胁。该病毒经历了多种进化途径。由于SARS-CoV-2大流行的持续进化，世界各地的研究人员正在努力通过运用深度学习和机器学习方法来减轻、抑制它的传播，并更好地了解它。摘要:DNA序列分类是生物信息学领域的一个重要研究课题。近年来,人们采用了多种机器技术和深度学习技术来完成这一任务,并取得了一定的成功。DNA序列分类是生物信息学领域的一个重要研究领域,因为它可以帮助研究人员进行基因组分析和检测可能的疾病。本文主要介绍了DNA序列分类的研究进展。本文利用两种DNA序列转换方法,提出了三种最先进的基于深度学习的DNA序列分类模型,提出了一种新的多变压器深度学习模型和成对特征融合技术用于DNA序列分类。从多转换器的最后一层提取深层特征，并将其应用于DNA序列分类的机器学习模型中，提出了K-Mer和One Hot编码序列转换技术，该多转换器在COVID DNA序列分类中取得了最高的性能，自动识别和分类病毒对于避免像COVID-19这样的病毒爆发至关重要。
Abstract
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.”
Key words
London/United Kingdom/Europe/COVID-19/Coronavirus/Cyborgs/DNA Research/DNA Sequence Proteomics/Deep Learning/De oxyribonucleic Acid/Emerging Technologies/Genetics/Health and Medicine/Machi ne Learning/RNA Viruses/Respiratory Tract Diseases and Conditions/Risk and Pr evention/SARS/SARS-CoV-2/Severe Acute Respiratory Syndrome/Severe Acute Resp iratory Syndrome Coronavirus 2/Viral/Virology
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