{"id":65913,"date":"2024-01-26T10:51:07","date_gmt":"2024-01-26T09:51:07","guid":{"rendered":"https:\/\/www.embl.org\/news\/?p=65913"},"modified":"2024-01-26T10:51:13","modified_gmt":"2024-01-26T09:51:13","slug":"proteomicsml-empowering-proteomics-research-with-machine-learning","status":"publish","type":"post","link":"https:\/\/www.embl.org\/news\/science\/proteomicsml-empowering-proteomics-research-with-machine-learning\/","title":{"rendered":"ProteomicsML: empowering proteomics research with machine learning"},"content":{"rendered":"\n<article class=\"vf-card vf-card--brand vf-card--bordered vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      Summary    <\/h3>\n                <p class=\"vf-card__text\"><ul>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ProteomicsML is a unified platform to streamline how researchers access proteomics data for machine learning applications.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The platform enhances accessibility and reproducibility in proteomics research by providing comprehensive tutorials.<\/span><\/li>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ProteomicsML is a community-driven project and encourages contributions that expand and evolve with the rapidly advancing field of proteomics.<\/span><\/li>\r\n<\/ul><\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<p>ProteomicsML \u2013 a free online resource \u2013 aims to simplify the complex, time-consuming process of having available proteomics datasets that train machine learning algorithms. By acting as a centralised platform, ProteomicsML aims to make proteomics research more accessible and reproducible. The platform also contains a range of freely available tutorials to aid proteomics research for both experienced scientists and newcomers to the field.<\/p>\n\n\n\n<a href=\"https:\/\/proteomicsml.org\/\" target=\"_blank\">\n<button class=\"vf-button vf-button--primary vf-button--sm\">Access ProteomicsML<\/button>\n<\/a>\n<!--\/vf-button-->\n\n\n\n\n<div style=\"height:12px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>&#8220;ProteomicsML emerged as a community-driven project,\u201d said <a href=\"https:\/\/www.ebi.ac.uk\/people\/person\/juan-vizcaino\/\">Juan Antonio Vizcaino, Team Leader, Proteomics at EMBL\u2019s European Bioinformatics Institute (EMBL-EBI)<\/a>. \u201cThe aim of the platform is to promote AI and machine learning applications for mass spectrometry-based proteomics data. The community will&nbsp; create and document training datasets and tutorials, making ProteomicsML a vital resource for anyone new to the field or looking to apply machine learning to proteomics data.&#8221;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Proteomics data processing<\/h2>\n\n\n\n<p>Preparing proteomics data for machine learning is complex and time-consuming. Different labs use varied methods, making it hard to share and use data widely. ProteomicsML tackles this challenge by offering an online platform with easy-to-use data formats and detailed tutorials to aid accessibility across the field.<\/p>\n\n\n\n<p>ProteomicsML also facilitates the application of machine learning to proteomics data by offering openly available datasets that can be used to train machine learning algorithms, and providing educational materials to help researchers get the most out of these datasets. The resource covers a wide range of data types including ion fragmentation intensity, ion mobility, retention time, protein detectability, and more, making ProteomicsML a valuable tool for the proteomics community and also for AI practitioners.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Expansion and community contribution<\/h2>\n\n\n\n<p>ProteomicsML is designed to evolve with the field. It encourages community contributions, allowing researchers to upload data and tutorials on their data handling and machine learning methodologies.&nbsp;<\/p>\n\n\n\n<p>ProteomicsML also stands as a comprehensive resource for those working with machine learning methods to analyse mass spectrometry proteomics data. By providing multiple datasets on a range of liquid chromatography and mass spectrometry peptide properties, it offers an accessible starting point for newcomers to the field.&nbsp;<\/p>\n\n\n\n<article class=\"vf-card vf-card--brand vf-card--bordered vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      Community-driven resource    <\/h3>\n                <p class=\"vf-card__text\"><span style=\"font-weight: 400;\">ProteomicsML was created by the <\/span><a class=\"vf-card_link\" href=\"https:\/\/www.sdu.dk\/en\"><span style=\"font-weight: 400;\">University of Southern Denmark (SDU)<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a class=\"vf-card_link\" href=\"https:\/\/www.compomics.com\/\"><span style=\"font-weight: 400;\">CompOmics<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a class=\"vf-card_link\" href=\"https:\/\/www.lumc.nl\/en\/\"><span style=\"font-weight: 400;\">Leiden University Medical Center (LUMC)<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a class=\"vf-card_link\" href=\"https:\/\/peptideatlas.org\/\"><span style=\"font-weight: 400;\">PeptideAtlas<\/span><\/a><span style=\"font-weight: 400;\">, the <\/span><a class=\"vf-card_link\" href=\"https:\/\/www.nist.gov\/\"><span style=\"font-weight: 400;\">National Institute of Standards and Technology (NIST)<\/span><\/a><span style=\"font-weight: 400;\">, the <\/span><a class=\"vf-card_link\" href=\"https:\/\/www.ebi.ac.uk\/pride\/\"><span style=\"font-weight: 400;\">PRoteomics IDEntification database (PRIDE)<\/span><\/a><span style=\"font-weight: 400;\">, and <\/span><a class=\"vf-card_link\" href=\"https:\/\/www.msaid.de\/\"><span style=\"font-weight: 400;\">MSAID<\/span><\/a><span style=\"font-weight: 400;\">. It was a direct output from a workshop held at the Lorentz Center, in Leiden.<\/span><\/p>\n      <\/div>\n<\/article>\n\n","protected":false},"excerpt":{"rendered":"<p>Streamlining proteomics data access for machine learning applications.<\/p>\n","protected":false},"author":77,"featured_media":65915,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2,11056],"tags":[4718,28,36,604,248,45],"embl_taxonomy":[2906,11096],"class_list":["post-65913","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science","category-technology-and-innovation","tag-artificial-intelligence","tag-bioinformatics","tag-embl-ebi","tag-machine-learning","tag-mass-spectrometry","tag-proteomics","embl_taxonomy-embl-ebi","embl_taxonomy-embl-ebi-proteomics-metabolomics"],"acf":{"featured":true,"show_featured_image":false,"field_target_display":"both","field_article_language":{"value":"english","label":"English"},"article_intro":"<p>Streamlining proteomics data access for machine learning 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