[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126227-en":3,"doc-seo-126227-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126227,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Using Blood Metabolomics to Identify Dietary Protein Intake with Machine Learning Methods - Master’s Thesis 2023","This master’s thesis investigates how metabolomics data can classify individuals according to dietary protein sources. The study aims to develop accurate models to distinguish omnivores, vegans, vegetarians, and pescetarians. Principal Component Analysis (PCA), Random Forest (RF), Support Vector Machines (SVM), and neural networks are evaluated. A scaled dataset with blood samples from 120 healthy participants contains unidentified metabolites. PCA shows substantial individual variation and limited separation of food groups, while RF achieves strong accuracy for omnivore vs non-omnivore classification.","Using blood metabolomics to identify dietary protein intake with Machine Learning methods  \nMaster’s thesis in Computer science and engineering  \nKLEIO GKOUTZOMITROU  \nDepartment of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY UNIVERSITY OF GOTHENBURG  \nGothenburg, Sweden 2023  \nMaster’s thesis 2023  \nUsing blood metabolomics to identify dietary protein intake with Machine Learning methods  \nKLEIO GKOUTZOMITROU  \nDepartment of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2023  \nA Chalmers University of Technology Master’s thesis template for LATEX  \nKLEIO GKOUTZOMITROU  \n© KLEIO GKOUTZOMITROU, 2023 .  \nSupervisor: Annikka Polster, Department of Biology and Biological Engineering Advisor: Helen Lindqvist, Biochemistry and Food Science (University of Gothenburg)  \nExaminer: Jean-Philippe Bernardy, Department of Computer Science and Engineering  \nMaster’s Thesis 2023  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg  \nTelephone +46 31 772 1000  \nCover: Description of the picture on the cover page (if applicable)  \nTypeset in LATEX  \nGothenburg, Sweden 2023  \nA Chalmers University of Technology Master’s thesis template for LATEX  \nKLEIO GKOUTZOMITROU  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg  \nAbstract  \nThis thesis examines how metabolomics data may be used to classify individuals based on the sources of protein in their diets. Developing accurate classification models that can distinguish between omnivores, vegans, vegetarians, and pescetarians is the aim of the study. Principal Component Analysis (PCA), Random Forest (RF), Support Vector Machines (SVM), and neural networks are used in this process as data analysis tools.  \nThe dataset, which was given by the Gothenburg University Department of Internal Medicine and Clinical Nutrition, included 120 healthy participants who followed various eating patterns. The subjects were chosen based on certain criteria, and blood samples and body composition were taken and examined. The dataset has been scaled and contains unidentified metabolites.  \nThe metabolic profile of the sample was shown using principal component analysis (PCA) . The overall PCA analysis revealed that there was substantial individual variation in the metabolomic profiles and that the food groups could not be effectively differentiated. The metabolic profiles of meat eaters and non-meat eaters might be used to distinguish them.  \nRandom Forest, SVM, and neural networks were the three machine learning techniques that were utilized for categorization. Neural Networks performed worse than Random Forest and SVM models in classifying each dietaryăgroup separately. Random Forest classified omnivores and non-omnivores with a high degree of accuracy.  \nTo measure the consumption of dairy, eggs, and meat, several scoring techniques were applied. The second method, which increased meat intake ratings by a factor of 1.5, produced the results with the highest degree of accuracy.  \nThis study sheds light on the metabolic effects of omnivorous diets and improvesour understanding of the complex relationship between nutrition, metabolism, and health outcomes. It also highlights the potential of metabolomics and machine learning in predicting dietary patterns and categorizing people into different dietary categories.  \nKeywords: metabolomics, machine learning, Principal Component Analysis, Random Forest, Support Vector Machines, Neural Networks.  \nAcknowledgements  \nI would like to express my heartfelt gratitude to Annikka Polster, my supervisor, for her unwavering support, guidance, and valuable input throughout this project. Her expertise and mentorship have been instrumental in shaping the direction and execution of this thesis.  \nI am also deeply grateful to Helen Lindqvist for generously providing m","cbCaisMtgGsPjJSB","https://ap.wps.com/l/cbCaisMtgGsPjJSB","pdf",2078574,6,1,63,"English","en",105,"# Introduction\n## Background\n## Methods\n## Structure\n# Theory\n## Diet\n## Metabolomics\n## Nuclear Magnetic Resonance (NMR)\n## Statistical Methods\n### Principal Component Analysis (PCA)\n## Machine Learning\n### Supervised Learning\n### Unsupervised Learning\n### Semi-supervised Learning\n### Reinforcement Learning\n### Applications of Machine Learning\n### Machine Learning Algorithms\n### Random Forest\n### Support Vector Machine\n### Neural Networks\n### Cross Validation\n# Methods\n## Data Collection","[{\"question\":\"What classification task does the thesis focus on?\",\"answer\":\"The thesis classifies individuals based on the sources of protein in their diets, targeting groups such as omnivores, vegans, vegetarians, and pescetarians.\"},{\"question\":\"Which machine learning and data analysis methods are used?\",\"answer\":\"Principal Component Analysis (PCA), Random Forest (RF), Support Vector Machines (SVM), and neural networks are used to analyze metabolomics data and build classification models.\"},{\"question\":\"What do the results suggest about model performance?\",\"answer\":\"PCA indicates substantial individual variation and limited differentiation among food groups, while Random Forest provides high accuracy for distinguishing omnivores from non-omnivores. Neural networks perform worse than RF and SVM for separately classifying dietary categories.\"}]","Using Blood Metabolomics to Identify Dietary Protein Intake with Machine Learning Methods - Master’s Thesis 2023 | PDF",1785903927,159,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"using-blood-metabolomics-to-identify-dietary-protein-intake-with-machine-learning-methods-masters-thesis-2023","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/using-blood-metabolomics-to-identify-dietary-protein-intake-with-machine-learning-methods-masters-thesis-2023/126227/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What classification task does the thesis focus on?","Question",{"text":77,"@type":78},"The thesis classifies individuals based on the sources of protein in their diets, targeting groups such as omnivores, vegans, vegetarians, and pescetarians.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning and data analysis methods are used?",{"text":82,"@type":78},"Principal Component Analysis (PCA), Random Forest (RF), Support Vector Machines (SVM), and neural networks are used to analyze metabolomics data and build classification models.",{"name":84,"@type":75,"acceptedAnswer":85},"What do the results suggest about model performance?",{"text":86,"@type":78},"PCA indicates substantial individual variation and limited differentiation among food groups, while Random Forest provides high accuracy for distinguishing omnivores from non-omnivores. 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