[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290146-105":3,"doc-detail-290146-en":73,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":66,"head_meta":68,"extra_data":70,"updated_unix":72},105,"en","toward-an-integrated-machine-learning-model-of-a-proteomics-experiment","Toward an Integrated Machine Learning Model of a Proteomics Experiment","","Machine learning has advanced significantly in modeling aspects of mass spectrometry data, enabling more realistic representations of multidimensional LC-MS/MS proteomics workflows. The perspective summarizes a workshop that aligned proteomics data generators, repository managers, and machine learning experts to evaluate applications and map a sample-to-data roadmap. It highlights synthetic data generation for system suitability, method development, and algorithm benchmarking, while raising ethical and research-integrity questions. The discussion reviews present capabilities and future opportunities and challenges for machine learning in proteomics.",{"@graph":14,"@context":65},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/toward-an-integrated-machine-learning-model-of-a-proteomics-experiment/290146/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/toward-an-integrated-machine-learning-model-of-a-proteomics-experiment/290146.png","ImageObject",300,407,{"name":42,"@type":43},"Jiven","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59],{"name":60,"@type":61,"acceptedAnswer":62},"What are the main uses of bespoke realistic synthetic proteomics data mentioned in the text?","Question",{"text":63,"@type":64},"Synthetic data supports system suitability, method development, and algorithm benchmarking, including creating sample-specific ground truth for evaluation.","Answer","https://schema.org",{"og:url":32,"og:type":67,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":69,"canonical":32},"index,follow",{"doc_id":71,"site_id":7},290146,1789640800,{"code":4,"msg":74,"data":75},"success",{"doc_id":71,"user_id":76,"nickname":42,"user_avatar":77,"doc_module":4,"category_id":78,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":79,"file_id":80,"file_url":81,"file_type":82,"file_size":83,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":84,"language":85,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":86,"faqs":87,"seo_title":88,"seo_description":12,"update_tm":72,"read_time":89,"preview_page_count":4},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Toward an integrated machine Learning model of a proteomics experiment  \nNeely, B.A.; Dorfer, V.; Martens, L.; Bludau, I.; Bouwmeester, R.; Degroeve, S.; ... ; Palmblad, M.  \nCitation  \nNeely, B. A., Dorfer, V., Martens, L., Bludau, I., Bouwmeester, R., Degroeve, S.,…Palmblad, M. (2023) . Toward an integrated machine Learning model of a proteomics experiment. Journal Of Proteome Research, 22(3), 681-696. doi:10.1021/acs.jproteome.2c00711  \nVersion: Publisher's Version  \nLicense:  Creative Commons CC BY 4.0 license  \nDownloaded from:  [https://hdl.handle.net/1887/3590801](https://hdl.handle.net/1887/3590801)  \nNote: To cite this publication please use the final published version (if applicable) .  \n[pubs.acs.org/jpr](pubs.acs.org/jpr)  Perspective   \nDownloaded via LEIDEN UNIV on June 2, 2023 at 11:59:30 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nToward an Integrated Machine Learning Model of a Proteomics Experiment  \nBenjamin A. Neely, * Viktoria Dorfer, * Lennart Martens, * Isabell Bludau, Robbin Bouwmeester, Sven Degroeve, Eric W. Deutsch, Siegfried Gessulat, Lukas Käll, Pawel Palczynski, Samuel H. Payne, Tobias Greisager Rehfeldt, Tobias Schmidt, Veit Schwämmle, Julian Uszkoreit, Juan Antonio Vizcaíno, Mathias Wilhelm, and Magnus Palmblad *  \n Cite This: J. Proteome Res. 2023, 22, 681−696  \nRead Online  \n\n|  |  |  |  |\n| --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |\n\nABSTRACT: In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.  \nKEYWORDS: machine learning, deep learning, artificial intelligence, synthetic data, enzymatic digestion, liquid chromatography, ion mobility, tandem mass spectrometry, research integrity  \n■ INTRODUCTION  \nAnalytical workflows in proteomics frequently rely on analyzing proteins or peptides by liquid chromatography (LC) and tandem mass spectrometry (MS/MS). Machine learning has been applied to predicting peptide retention times and fragmentation spectra, but recent advances in deep learning have dramatically improved these predictions,1−3 as well as modeling other experimental steps, such as enzymatic digestion4,5 and ion mobility.6 Common to all steps is that the behavior of the proteins, peptides or ions can be predicted from amino acid sequences. However, no model exists that can make realistic predictions of data from a multidimensional separation and analysis of any sample or organism.  \nMachine learning is set to revolutionize the generation of realistic multidimensional data from arbitrary samples. There are several general application areas of such generative models. For example, when acquiring LC-MS/MS data, acquisition parameters are usually chosen from heuristics and past experience. A machine-learned model predicting LC-MS/MS data for diverse sample types in proteomics could replace guesswork and enable optimal experimental design, including for single cell analysis and o","cbCaihMgcCCiW51U","https://ap.wps.com/l/cbCaihMgcCCiW51U","pdf",2085402,17,"English","# Abstract\n# Introduction\n## Generative models for multidimensional proteomics data\n## Applications: benchmarking, system suitability, and quality control\n## Need for integrated end-to-end modeling","[{\"question\":\"What are the main uses of bespoke realistic synthetic proteomics data mentioned in the text?\",\"answer\":\"Synthetic data supports system suitability, method development, and algorithm benchmarking, including creating sample-specific ground truth for evaluation.\"}]","Toward an Integrated Machine Learning Model of a Proteomics Experiment | PDF",43,{"code":4,"msg":74,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":78,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":25,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]