[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117976-en":3,"doc-seo-117976-105":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},117976,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders - Abstract and Study Overview","Metabolomics is positioned as a high-potential discipline for pharmaceutical research and preventive healthcare, especially for disease detection and drug testing. Large metabolomics datasets are difficult to analyze, because existing methods often depend on limited and incompletely annotated pathways. This study proposes machine-learning classifiers trained on metabolite molecular fingerprints to predict responses under defined experimental conditions, evaluated on mass-spectrometry data from a cellular model of Ataxia Telangiectasia.","Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders  \nChristel Sirocchi1,∗,†, Federica Biancucci2,†, Matteo Donati1 , Nunzio D’Amore1 , Riccardo Benedetti2, Alessandro Bogliolo1 , Stefano Ferretti1 , Mauro Magnani2, Michele Menotta2, Muhammad Suffian1 and Sara Montagna1  \n1 Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica 13, 61029, Urbino, Italy 2 Department of Biomolecular Sciences, University of Urbino, Via Saffi 2, 61029 Urbino, Italy  \nAbstract  \nMetabolomics has emerged as a promising discipline in pharmaceuticals and preventive healthcare, holding great potential for disease detection and drug testing. However, analysing large metabolomics datasets remains challenging, with available methods generally relying on limited and incompletely annotated biological pathways. This study introduces a novel approach that leverages machine learning classifiers trained on molecular fingerprints of metabolites, to predict their responses under specific experimental conditions. The model is evaluated on mass spectrometry metabolomic data for a cellular model of the genetic disease Ataxia Telangiectasia. In this study, metabolite structures are encoded using the Morgan fingerprint, a well-established technique widely embraced in drug discovery. The suitability of this fingerprinting method, in generating unique structural encodings for detected metabolites, is analysed, and strategies to mitigate resolution limitations inherent to this fingerprint are introduced. Machine learning classifiers are trained on these fingerprints and exhibit satisfactory performance, providing evidence that the structural encoding holds predictive power over the metabolic response. Feature importance analysis, conducted on the best-performing models, identifies the chemical configurations that have the greatest influence to the classification process, shedding light on affected biological processes. Remarkably, this analysis not only identifies metabolites known to participate in affected pathways but also discovers metabolites not previously associated with the disease, opening up novel opportunities for further exploration. As an initial exploration of the proposed approach, this work lays the foundation for future research that leverages alternative structural encodings, diverse machine learning models, and explainability tools.  \nKeywords  \nAtaxia telangiectasia, mass spectrometry, metabolic pathways, metabolomics, machine learning  \n1. Introduction  \nMetabolomics, as the quantitative study of small molecule substrates and products of cellular metabolism, occupies a unique position in the-omics landscape due to its proximity to the phenotype [1] . The metabolome, representing the final product of genomic, transcriptomic, and proteomic processes, provides a direct readout of the physiological state of an organism [2] .  \nSecond AIxIA Workshop on Artificial Intelligence For Healthcare, November 6, 2023, Rome, Italy  \n∗Corresponding author.  \n†  \nThese authors contributed equally.  \n[Envelope-Open](Envelope-Open c.sirocchi2@campus.uniurb.it)[ c.sirocchi2@campus.uniurb.it](Envelope-Open c.sirocchi2@campus.uniurb.it) (C. Sirocchi); [federica.biancucci@uniurb.it](federica.biancucci@uniurb.it) (F. Biancucci)  \nOrcid 0000-0002-5011-3068 (C. Sirocchi); 0009-0006-2567-5460 (F. Biancucci)  \n © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) . CWPEURorkroceshopedings http://ceurISSN 1613-ws-0073.org CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org))  \nCEUR ~~  ~~[Workshop](Workshop ceur-ws.org)[ ceur-ws.org](Workshop ceur-ws.org)[ ](Workshop ceur-ws.org)[Proceedings](Proceedings ISSN 1613-0073)[ ISSN 1613-0073](Proceedings ISSN 1613-0073)   \nMetabolomic profiling of diseased and healthy tissues can help uncover the disease mechanisms of action and identify metabolic signatures, aiding the identification of potent","cbCaiuqhLONX155u","https://ap.wps.com/l/cbCaiuqhLONX155u","pdf",371442,1,9,"English","en",105,"# Introduction\n## Metabolomics and mass spectrometry\n## Challenges of pathway enrichment\n## Study rationale and approach","[{\"question\":\"What problem does the study address in metabolomics data analysis?\",\"answer\":\"It addresses the difficulty of analyzing large metabolomics datasets, where many existing methods rely on limited and incompletely annotated biological pathways.\"},{\"question\":\"How does the proposed method predict metabolite response?\",\"answer\":\"It trains machine-learning classifiers on molecular fingerprints of metabolites derived from the Morgan fingerprint representation.\"},{\"question\":\"What insights does feature importance provide in the study?\",\"answer\":\"Feature importance analysis highlights chemical configurations that most influence classification, identifying known metabolites in affected pathways and suggesting metabolites not previously associated with the disease.\"}]","Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders - 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