[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118137-en":3,"doc-seo-118137-105":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":4,"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},118137,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Using machine learning for source attribution of Listeria monocytogenes","This master’s thesis develops machine-learning models to predict the source of new infections caused by Listeria monocytogenes. The work combines epidemiological source attribution needs with genomic data from Norwegian whole-genome sequenced isolates with known sources. Genomic diversity is assessed to determine whether isolates can be partitioned by source using different representations and gene subsets. Results indicate that allelic profiles from core-genome and whole-genome MLST provide input diversity for source attribution, with Random Forest performing best for food-associated sources and weaker for non-food-associated sources.","Master’s Thesis 2024 60 ECTS  \nFaculty of Chemistry, Biotechnology and Food Science  \nUsing machine learning for source attribution of Listeria monocytogenes  \nTerese Ryan Andersen Bioinformatics and Applied Statistics  \nAcknowledgements  \nThis thesis is a part of the master’s degree in Bioinformatics and Applied Statistics at the Faculty of Chemistry, Biotechnology, and Food Science (KBM) at the Norwegian University of Life Science (NMBU) . The study conducted for this master ’s thesis was done in collaboration with the section for Epidemiology at the Norwegian Veterinary Institute.  \nI would like to thank my supervisor Karin Lagesen at the Norwegian Veterinary Institute. Iam grateful for all the guidance and knowledge you have shared with me, and for all the meaningful discussions and help you have provided throughout this work.  \nI would also like to thank my supervisor Lars-Gustav Snipen at the Norwegian University of Life Science for all the feedback you have given me and helping me to improve my work.  \nLastly, I would like to thank my family and friends for the support throughout this year, especially my boyfriend Jonas Antonsen for always believing in me.  \nÅs, May 2024  \nTerese Ryan Andersen  \nAbstract  \nIn this study source attribution was combined with machine learning for the purposes of making models that can predict the source of new cases of infection caused by Listeria monocytogenes. L. monocytogenes causes the infection listeriosis in humans, and the main source of infection is through food. Although it is considered a low pathogenic bacterium, the mortality rate for infected humans makes it a public health issue. Listeriosis is particularly dangerous for the elderly, the immune suppressed and for pregnant individuals. A quick identification of the source of infection is key to stopping further spread of the bacteria. By using genomic data from L. monocytogenes with known sources, a machine learning model may be trained to classify the bacterial isolates by sources. The trained model can then predict the sources of new cases. The available information in the genomic data was also explored to investigate if it was diverse enough to be used for partitioning isolates by source. Machine learning has already shown potential for source attribution of L. monocytogenes and other pathogens in studies from other countries. The origin ofthe data set in this study was Norwegian and contained data of whole genome sequenced L. monocytogenes isolates. The possibility of separating the isolates and being able to predict the sources utilizing the genetic information were explored with different kinds of machine learning methods, representation of the genomes, and subsets ofthe genes in the data set. The results of this research suggest that allelic profiles from both core genome and whole genome Multi Locus Sequence Typing (MLST) methods gives input data that are diverse enough for machine learning models to use for source attribution. The machine learning method Random Forest could use the allelic profiles directly as input data and had good predicting performance for the isolates with foodassociated sources in this study but had poorer performance for the isolates with not foodassociated sources. The method Support Vector Machine needed scaling of the input data to predict well and had similar predicting performance as the Random Forest method. The last machine learning method in this study was a neural network which was the method with the highest use of time and computational resources. The neural network performed poorer than the other methods but showed potential and better predicting performance can possibly be obtained with more tuning to improve the model.  \nSammendrag  \nI denne studien er smittesporing kombinert med maskinlæring for å kunne lage modeller som kan predikere smittekilden til nye infeksjonstilfeller forårsaket av Listeria monocytogenes. Infeksjonen listeriose hos mennesker er forårsaket av L. monocytogen","cbCaisWMleQ3aizP","https://ap.wps.com/l/cbCaisWMleQ3aizP","pdf",1949310,1,101,"English","en",105,"# Abstract\n## Study aim and data\n## Modeling approach\n## Results by machine learning method\n## Conclusions and future tuning","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis targets source attribution for new Listeria monocytogenes infection cases, aiming to predict the likely source of infection using genomic information.\"},{\"question\":\"What dataset is used for training and evaluation?\",\"answer\":\"It uses Norwegian whole-genome sequenced L. monocytogenes isolates where the infection sources are known.\"},{\"question\":\"Which machine learning methods were compared and what were the main differences?\",\"answer\":\"Random Forest used allelic profiles directly and predicted well for food-associated sources but worse for non-food-associated sources. Support Vector Machine required input scaling, while the neural network used the most time and computational resources and performed poorer than the other methods, though it showed potential with further tuning.\"}]","Using machine learning for source attribution of Listeria monocytogenes | PDF",1785681810,255,{"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},"using-machine-learning-for-source-attribution-of-listeria-monocytogenes","",{"@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/using-machine-learning-for-source-attribution-of-listeria-monocytogenes/118137/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address?","Question",{"text":75,"@type":76},"The thesis targets source attribution for new Listeria monocytogenes infection cases, aiming to predict the likely source of infection using genomic information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used for training and evaluation?",{"text":80,"@type":76},"It uses Norwegian whole-genome sequenced L. monocytogenes isolates where the infection sources are known.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods were compared and what were the main differences?",{"text":84,"@type":76},"Random Forest used allelic profiles directly and predicted well for food-associated sources but worse for non-food-associated sources. Support Vector Machine required input scaling, while the neural network used the most time and computational resources and performed poorer than the other methods, though it showed potential with further tuning.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]