[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122590-en":3,"doc-seo-122590-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122590,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Unsupervised machine learning framework for discriminating major variants of concern during COVID-19","High mutation rates enabled COVID-19 to rapidly evolve into major variants including Alpha, Gamma, Delta, Beta, and Omicron, each with distinct viral properties and major consequences for healthcare and global economic stability. The framework uses unsupervised machine learning to compress, characterize, and visualize unlabeled genomic information. RNA sequences are transformed via k-mer analysis, then compared across dimensionality reduction methods (PCA, t-SNE, UMAP) and visualized using agglomerative hierarchical clustering and dendrograms to reveal mutational and country-level differences.","Unsupervised machine learning framework for discriminating major variants of concern  \nduring COVID-19  \nRohitash Chandraa,1 , Chaarvi Bansalc , Mingyue Kanga , Tom Blaud , Vinti Agarwalc , Pranjal Singhe , Laurence O. W. Wilsonb ,  \nSeshadri Vasanf  \na Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney, Sydney, Australia b Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia c Department of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Rajasthan, India  \nd Data61, CSIRO, Sydney, Australia  \ne Department of Computer Science and Engineering, Indian Institute of Technology Guwathi, Assam, India  \nf Department of Health Sciences, University of York, York, United Kingdom  \nAbstract  \nDue to high mutation rates, COVID-19 evolved rapidly, and several variants such as Alpha, Gamma, Delta, Beta, and Omicron emerged with altered viral properties like the severity of the disease caused, transmission rates, etc. These variants burdened the medical systems worldwide and created a massive impact on the world economy as each had to be studied and dealt with in its specific ways. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and then compares the results from di􀀋erent dimensionality reduction methods including: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation Projection (UMAP) . Our framework also employs agglomerative hierarchical clustering to visualize the mutational di􀀋erences among major variants of concern and country-wise mutational di􀀋erences for a particular variant (Delta and Omicron) using dendrograms. We conclude that the proposed framework can e􀀋ectively distinguish between the major variants and hence can be used for the identification of emerging variants in the future.  \nKeywords: SARS-CoV-2, Mutation, COVID-19, unsupervised machine learning, clustering, PCA, t-SNE, UMAP  \n1. Introduction  \nCoronaviruses (CoVs) consist of enclosed, positive-sense, single-stranded and diversified Ribonucleic acid (RNA) viruses [1] . CoVs comprise major variants that occur through mutations, also known as genera, including delta, gamma, beta and alpha [2, 3] . Among these, the alpha-genera, also known ashCoV-NL63 and hCoV-229E [4] and beta-genera garner more attention due to their capability to transmit from animal to human and exist as human coronaviruses (hCoVs) [5] . These are particularly obvious in the beta-genera CoVs, which account for Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV)[6] . The associated risks triggered by the COVID-19 were more severe compared to MERS-CoV with an increased rate of infections and deaths. Even though hCoVs primarily lead to asymptomatic or mild infections, they have been transmitting in humans since they were discovered, and cause around 15 to 30% of common colds [7] . Nevertheless, scientists have not taken hCoVs as a  \nsevere problem until the world witnessed the worldwide pandemic caused by SARS-CoV-2 and the detrimental repercussions to the world economy [8, 9] .  \nCurrently, there are three reported highly deadly coronaviruses, MERS-CoV, SARS-CoV-2 and SARS-CoV due to their lethal e􀀋ects on homo-sapiens [10, 11] . In contrast to other hCoVs, these three are more likely to cause acute lung injury (ALI), multiple organ failure and even death [12] . A dreadful pandemic surfaced ten ","cbCaiiwi7VJ3ShGd","https://ap.wps.com/l/cbCaiiwi7VJ3ShGd","pdf",651048,1,12,"English","en",105,"# Introduction\n## COVID-19 variants and mutational impact\n## Role of unsupervised learning for genomic data\n## Dimensionality reduction and clustering approach","[{\"question\":\"What problem does the framework address for COVID-19 research?\",\"answer\":\"It targets the need to discriminate and visualize associations between major SARS-CoV-2 variants using genomic sequences, focusing on how mutations differ across variants and countries.\"},{\"question\":\"How are RNA genome sequences processed in the proposed approach?\",\"answer\":\"The method performs k-mer analysis on the RNA sequences, generating representations that can be compared across dimensionality reduction techniques.\"},{\"question\":\"Which analytical techniques are combined to visualize variant differences?\",\"answer\":\"It integrates dimensionality reduction methods including PCA, t-SNE, and UMAP, and uses agglomerative hierarchical clustering with dendrograms to visualize mutational differences.\"}]","Unsupervised machine learning framework for discriminating major variants of concern during COVID-19 | PDF",1785811625,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"unsupervised-machine-learning-framework-for-discriminating-major-variants-of-concern-during-covid-19","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/unsupervised-machine-learning-framework-for-discriminating-major-variants-of-concern-during-covid-19/122590/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the framework address for COVID-19 research?","Question",{"text":75,"@type":76},"It targets the need to discriminate and visualize associations between major SARS-CoV-2 variants using genomic sequences, focusing on how mutations differ across variants and countries.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are RNA genome sequences processed in the proposed approach?",{"text":80,"@type":76},"The method performs k-mer analysis on the RNA sequences, generating representations that can be compared across dimensionality reduction techniques.",{"name":82,"@type":73,"acceptedAnswer":83},"Which analytical techniques are combined to visualize variant differences?",{"text":84,"@type":76},"It integrates dimensionality reduction methods including PCA, t-SNE, and UMAP, and uses agglomerative hierarchical clustering with dendrograms to visualize mutational differences.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]