{"id":257061,"date":"2025-07-28T09:48:51","date_gmt":"2025-07-28T09:48:51","guid":{"rendered":"https:\/\/project.uniurb.it\/vitality\/?p=257061"},"modified":"2025-07-28T14:30:27","modified_gmt":"2025-07-28T14:30:27","slug":"exploring-machine-learning-for-untargeted-metabolomics-using-molecular-fingerprints","status":"publish","type":"post","link":"https:\/\/project.uniurb.it\/vitality\/exploring-machine-learning-for-untargeted-metabolomics-using-molecular-fingerprints\/","title":{"rendered":"Exploring machine learning for untargeted metabolomics using molecular fingerprints"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;2px|||||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;50px|||||&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; text_font_size=&#8221;16px&#8221; custom_margin=&#8221;||60px||false|false&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">Dott.ssa Sara Montagna<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; locked=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3>Abstract<\/h3>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">Background: Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism\u2019s state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. Methods: This study, inspired by well-established methods in drug discovery, employs machine learning on <\/span><span style=\"font-weight: 400;\">metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups. Results: The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study. Conclusion: In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||0px|||&#8221; locked=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h4>Keyword<\/h4>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">Ataxia telangiectasia, Mass spectrometry, Molecular fingerprinting, Untargeted metabolomics Machine learning<\/span><\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||0px|||&#8221; locked=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h4>Link<\/h4>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; link_option_url_new_window=&#8221;on&#8221; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;]<\/p>\n<p><a href=\"https:\/\/ora.uniurb.it\/item\/preview.htm?uuid=f487349e-f5a4-4c45-bb87-4079e5413d51\" target=\"_blank\" rel=\"noopener\">https:\/\/ora.uniurb.it\/item\/preview.htm?uuid=f487349e-f5a4-4c45-bb87-4079e5413d51<\/a><\/p>\n<p><a href=\"https:\/\/hdl.handle.net\/11576\/2734731\" target=\"_blank\" rel=\"noopener\" title=\"Exploring machine learning for untargeted metabolomics using molecular fingerprints\">https:\/\/hdl.handle.net\/11576\/2734731<\/a>\u00a0<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Dott.ssa Sara MontagnaAbstractBackground: Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism\u2019s state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. Methods: This study, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[19],"tags":[],"class_list":["post-257061","post","type-post","status-publish","format-standard","hentry","category-wp1"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Exploring machine learning for untargeted metabolomics using molecular fingerprints - Vitality<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/project.uniurb.it\/vitality\/exploring-machine-learning-for-untargeted-metabolomics-using-molecular-fingerprints\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Exploring machine learning for untargeted metabolomics using molecular fingerprints - Vitality\" \/>\n<meta property=\"og:description\" content=\"Dott.ssa Sara MontagnaAbstractBackground: Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism\u2019s state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. 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