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It contrasts simpler models that fit small datasets and can be more interpretable with advanced models that may better represent complex metabolite relationships but demand larger datasets and careful tuning. Even strong predictive metrics can misrepresent biological relevance, especially for high-dimensional, imbalanced data, so results require cautious interpretation with robust validation. Metabolomics data challenges, including missing values and technical variation, make preprocessing and study design central to reliable outcomes.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/metabolomics-based-biomarker-discovery-challenges-and-opportunities-with-machine-learning/290496/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/metabolomics-based-biomarker-discovery-challenges-and-opportunities-with-machine-learning/290496.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How do data characteristics influence machine learning model performance in metabolomics?","Question",{"text":112,"@type":113},"Data characteristics can determine how well models learn metabolite patterns. The study emphasizes that performance depends on dataset size, feature dimensionality, class balance, and data quality, which can strongly affect outcomes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why can good statistical performance fail to reflect true biological relevance?",{"text":117,"@type":113},"Evaluation metrics may indicate strong predictive ability even when metabolite relationships are not correctly captured. This is especially likely in high-dimensional and imbalanced datasets where models can overfit or exploit spurious correlations.",{"name":119,"@type":110,"acceptedAnswer":120},"What role do preprocessing and study design play in metabolomics-based biomarker discovery?",{"text":121,"@type":113},"Metabolomics data include high dimensionality, missing values, and technical variation. Because of this, data preprocessing is critical, and overall study design can have a greater impact on outcomes than the choice of the machine learning model alone.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290496,1789641842,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":36,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Metabolomics-Based Biomarker Discovery: Challenges and Opportunities with Machine Learning  \nMaryam Chaid  \nDegree project/Independent project • 15 hp Swedish University of Agricultural Sciences, SLUNJ Faculty  \nFood Science Agronomy Molecular Sciences, 2026:08 Uppsala, 2026  \nMetabolomics-Based Biomarker Discovery: Challenges and Opportunities with Machine Learning  \nMaryam Chaid  \nSupervisor: Ali Moazzami, department of Molecular Sciences  \nExaminer: Jan Eriksson, department of Molecular Sciences  \nCredits: 15  \nLevel: G2E  \nCourse title: Independent project in Food Science  \nCourse Code: EX0876  \nCourse coordinating dept: Department of Molecular Sciences  \nPlace of publication: Uppsala  \nTitle of series: Molecular Sciences  \nPart number: 2026:08  \nKeywords: analytical chemistry, biomarker discovery, deep learning,  \nmachine learning, metabolomics, systems biology, supervised learning, unsupervised learning  \nSwedish University of Agricultural Sciences  \nFaculty of Natural Resources and Agriculture Science (NJ) Department of Molecular Sciences  \nAbstract  \nThis literature study investigates the challenges and opportunities of using machine learning for biomarker discovery in metabolomics, with emphasis on how data characteristics influence model performance. In general, simple machine learning methods are well suited for small datasets and offer higher interpretability, but they may fail to capture complex biological relationships between metabolites, while more advanced methods can model such complexity but require larger datasets, careful tuning and often provide limited interpretability. Importantly, good statistical performance does not necessarily reflect true biological relevance, as evaluation metrics may indicate strong predictive ability even when underlying biological relationships are not properly captured, particularly in high-dimensional and imbalanced datasets. For this reason, model results must be interpreted with caution and supported by robust validation strategies, although commonly used validation approaches can still produce overly optimistic results if not correctly applied and more rigorous methods are often constrained by data availability and computational demands. At the sametime, metabolomics data itself presents major challenges due to high dimensionality, missing values and technical variation, which makes data preprocessing a critical step in ensuring reliable results. Overall, the findings suggest that in metabolomics-based biomarker discovery, data quality, preprocessing and study design have a greater impact on outcomes than the choice of machine learning model alone.  \nKeywords: analytical chemistry, biomarker discovery, deep learning, machine learning, metabolomics, systems biology, supervised learning, unsupervised learning  \nTable of contents  \nList of figures..................................................................................................................... 5  \nAbbreviations .................................................................................................................... 6  \n1. Introduction ............................................................................................................. 7  \n1.1 Metabolomics ............................................................................................................ 7  \n1.2 Machine Learning in Metabolomics .......................................................................... 9  \n1.3 Aim .......................................................................................................................... 10  \n2. Method ................................................................................................................... 12  \n2.1 Methodology............................................................................................................ 12  \n2.2 Literature Search Strategy ...................................................................................... 12  ","cbCaidgvWtg1sgBS","https://ap.wps.com/l/cbCaidgvWtg1sgBS","pdf",1362863,"English","# List of figures\n# Abbreviations\n# 1. Introduction\n## 1.1 Metabolomics\n## 1.2 Machine Learning in Metabolomics\n## 1.3 Aim\n# 2. Method\n## 2.1 Methodology\n## 2.2 Literature Search Strategy\n## 2.3 Selection Criteria\n## 2.4 Use of AI Tools\n## 2.5 Data Analysis\n# 3. Results\n## 3.1 Metabolomics Data and Preprocessing\n## 3.1.1 Data Acquisition and Characteristics\n## 3.1.2 Data Preprocessing\n## 3.2 Feature Engineering in Metabolomics\n## 3.2.1 Feature Selection\n## 3.2.2 Feature Extraction\n## 3.3 Statistical Learning in Metabolomics\n## 3.3.1 Univariate and Multivariate Analysis\n## 3.3.2 Machine Learning Methods\n## 3.3.3 Deep Learning Approaches\n## 3.4 Model Evaluation and Validation\n## 3.4.1 Evaluation Metrics\n## 3.4.2 Validation Strategies","[{\"question\":\"How do data characteristics influence machine learning model performance in metabolomics?\",\"answer\":\"Data characteristics can determine how well models learn metabolite patterns. The study emphasizes that performance depends on dataset size, feature dimensionality, class balance, and data quality, which can strongly affect outcomes.\"},{\"question\":\"Why can good statistical performance fail to reflect true biological relevance?\",\"answer\":\"Evaluation metrics may indicate strong predictive ability even when metabolite relationships are not correctly captured. This is especially likely in high-dimensional and imbalanced datasets where models can overfit or exploit spurious correlations.\"},{\"question\":\"What role do preprocessing and study design play in metabolomics-based biomarker discovery?\",\"answer\":\"Metabolomics data include high dimensionality, missing values, and technical variation. Because of this, data preprocessing is critical, and overall study design can have a greater impact on outcomes than the choice of the machine learning model alone.\"}]","Metabolomics-Based Biomarker Discovery - Challenges and Opportunities with Machine Learning | PDF",101]