[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127862-en":3,"doc-seo-127862-105":30,"detail-sidebar-cat-0-en-105":92},{"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},127862,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","What is a successful antibiotic resistance gene? - A conceptual model and machine learning predictions","Antibiotic resistance represents a major public health threat, driven by the horizontal gene transfer (HGT) that allows antibiotic resistance genes (ARGs) to spread between bacteria. This thesis develops a conceptual model of ARG success using four dissemination components across taxonomic barriers, GC environments, geographic regions, and pathogenic targets. Prevalence data from 4775 ARGs across 867,318 genomes supports success-score computation and class characterization. A random-forest binary classifier then predicts success from gene features under decreasing sample sizes, with performance improving as observations increase. The findings also highlight the value of enabling machine-learning–oriented antibiotic policies.","What is a successful antibiotic resistance gene? A conceptual model and machine learning predictions  \nMaster’s thesis in Biotechnology  \nELINOR EINARSSON & STINA TORELL  \nDEPARTMENT OF MATHEMATICAL SCIENCES  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2024  \n[www.chalmers.se](www.chalmers.se)  \nDegree project report 2024  \nWhat is a successful antibiotic resistance gene? A conceptual model and machine learning  \npredictions  \nELINOR EINARSSON  \nSTINA TORELL  \nDepartment of Mathematical Sciences Chalmers University of Technology Gothenburg, Sweden 2024  \nWhat is a successful antibiotic resistance gene? A conceptual model and machine learning predictions ELINOR EINARSSON & STINA TORELL  \n© ELINOR EINARSSON, 2024 .  \n© STINA TORELL, 2024 .  \nSupervisor: David Lund, Department of Mathematical Sciences Examiner: Erik Kristiansson, Department of Mathematical Sciences  \nDegree project report 2024 Department of Mathematical Sciences Chalmers University of Technology SE-412 96 Gothenburg  \nSweden  \nTelephone +46 31 772 1000  \nCover: Illustration of the conceptual model. Illustrated using BioRender.  \nTypeset in LATEX  \nGothenburg, Sweden 2024  \nWhat is a successful antibiotic resistance gene? A conceptual model and machine learning predictions ELINOR EINARSSON & STINA TORELL Department of Mathematical Sciences  \nChalmers University of Technology  \nAbstract  \nAntibiotic resistance is a global public health threat and it causes bacterial infections to become more difficult to treat. The spread of antibiotic resistance genes (ARGs) is predominantly driven by horizontal gene transfer (HGT) that enables bacteria to share genetic information directly between cells. The ability of an ARG to spread is influenced by a range of factors, and has become a popular field of research, aiming to find characteristics that enable rapid antibiotic resistance dissemination. This facilitates the identification of ARGs that possess the ability to disseminate rapidly, and for proactive measures against the dissemination to be implemented.  \nBioinformatics tools were used to study the prevalence of 4775 known ARGs in 867 318 bacterial genomes. A conceptual model describing the success of an ARG was developed containing four different measures of dissemination, over taxonomic barriers, in different GC-environments, geographical dissemination, and dissemination to pathogenic bacteria. By using a top-down approach studying the success of agene, the thesis complements research studying factors that characterizes successful and rapid HGT. The conceptual model resulted in a success-score for each ARG that reflected the overall performance in the four components. Among the ARGs found to be highly successful the most common class was multidrug resistance, followed by aminoglycoside, β-lactam, and MLS antibiotic resistance. Furthermore, the success-score together with information about the genes, were used to investigate the possibility to predict the success of an ARG with the use of machine learning in a binary classification Random forest algorithm. The model was built to evaluate the predictive performance using decreasing amounts of observations of each gene. As expected, the predictive performance of the model improved as the number of observation increased. Based on only one observation, it was possible to predict the class of each gene with an average sensitivity of ~70% at 90% specificity, and with 250 observations a sensitivity of 98% could be attained. Sequence related features such as gene length and codon usage were important when only a few observations of a gene were used, but as the number of observations grew, non-sequence related features such as number of countries and pathogens a gene was found in, became more relevant. A meta-analysis also aims to explore the managerial and policy implications of antibiotics resistance, and findings include that policies facilitating for machine learning are important to implement. This study can be use","cbCaioGjj08XL0Vk","https://ap.wps.com/l/cbCaioGjj08XL0Vk","pdf",4217578,1,76,"English","en",105,"# Introduction\n## Aim\n# Theory\n## Antibiotic resistance and gene dissemination\n### Horizontal gene transfer\n### Antibiotic classes and their mechanisms of action\n### Resistance mechanisms of antibiotic resistance genes\n## Machine Learning tools in bioinformatics\n### Building a Random forest algorithm\n### Predictions and Confusion Matrices\n### Receiver Operating Characteristics curve\n# Methods\n## Data collection\n## Conceptual model and score generation","[{\"question\":\"What determines the ability of an antibiotic resistance gene (ARG) to spread?\",\"answer\":\"ARG spread is shaped by multiple dissemination factors. The thesis models success using four components: dissemination across taxonomic barriers, in different GC environments, geographically, and to pathogenic bacteria.\"},{\"question\":\"How was ARG success quantified in the conceptual model?\",\"answer\":\"A conceptual model was built with four dissemination measures, and the overall performance across these components was combined into a success-score for each ARG.\"},{\"question\":\"How does machine learning predict ARG success and how does data size affect results?\",\"answer\":\"A random forest binary classifier used gene-related information to predict success. The predictive performance improves as the number of observations increases, reaching high sensitivity when more observations are available, while sequence and non-sequence features trade off in importance at different data sizes.\"}]","What is a successful antibiotic resistance gene? - A conceptual model and machine learning predictions | PDF",1785942403,192,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"what-is-a-successful-antibiotic-resistance-gene-a-conceptual-model-and-machine-learning-predictions","",{"@graph":36,"@context":86},[37,54,69],{"@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/what-is-a-successful-antibiotic-resistance-gene-a-conceptual-model-and-machine-learning-predictions/127862/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What determines the ability of an antibiotic resistance gene (ARG) to spread?","Question",{"text":76,"@type":77},"ARG spread is shaped by multiple dissemination factors. The thesis models success using four components: dissemination across taxonomic barriers, in different GC environments, geographically, and to pathogenic bacteria.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was ARG success quantified in the conceptual model?",{"text":81,"@type":77},"A conceptual model was built with four dissemination measures, and the overall performance across these components was combined into a success-score for each ARG.",{"name":83,"@type":74,"acceptedAnswer":84},"How does machine learning predict ARG success and how does data size affect results?",{"text":85,"@type":77},"A random forest binary classifier used gene-related information to predict success. 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