[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127020-en":3,"doc-seo-127020-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},127020,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","DOME Registry - Implementing community-wide recommendations for reporting supervised machine learning in biology","Supervised machine learning is widely used in biology yet requires closer scrutiny. The DOME recommendations strengthen validation and reproducibility by defining standards for data handling, processing, optimization, evaluation, and model interpretability, supported by a structured set of reporting questions. The DOME Registry is a database enabling researchers to manage and access DOME-related information for published ML studies. It uses tools such as ORCID, APICURON, and the Data Stewardship Wizard to streamline annotation, assign unique identifiers and DOME scores, and promote transparent assessment. Future work includes community curation and publisher adoption to further advance transparency and reproducibility in the life sciences.","DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning  \nin biology  \nOmar Abdelghani Attafi1,+ , Damiano Clementel1,+ , Konstantinos Kyritsis2 , Emidio Capriotti3 , Gavin Farrell4 , Styliani-Christina Fragkouli2,5 , Leyla Jael Castro6 , András Hatos7,8,9,10 , Tom Lenaerts11,12,13 , Stanislav Mazurenko14, 15 , Soroush Mozaffari1 , Franco Pradelli1 , Patrick Ruch16,17 , Castrense Savojardo3 , Paola Turina3 , Federico Zambelli18,19 , Damiano Piovesan1 , Alexander Miguel Monzon20 ,  \nFotis Psomopoulos2,*, Silvio C. E. Tosatto 1,21*  \n+These authors have equally contributed to this work  \n*corresponding authors  \n1Department of Biomedical Sciences, University of Padova, Italy,  \n2Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece 3Department of Pharmacy and Biotechnology. University of Bologna, Bologna, Italy  \n4ELIXIR Hub, Hinxton, Cambridge, UK  \n5Department of Biology, National and Kapodistrian University of Athens, Athens, Greece 6ZB Med Information Centre for Life Sciences, Cologne, Germany  \n7Department of Oncology, Geneva University Hospitals, Geneva, Switzerland 8Department of Computational Biology, University of Lausanne, Lausanne, Switzerland 9Swiss Institute of Bioinformatics, Lausanne, Switzerland  \n10Swiss Cancer Center Léman, Lausanne, Switzerland  \n11Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, 1050, Belgium  \n12Machine Learning Group, Université Libre de Bruxelles, Street, Belgium 13Artificial Intelligence Laboratory, Vrije Universiteit Brussels, Brussels, Belgium  \n14Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, 15Masaryk University, Brno, Czech Republic International Clinical Research Centre, St. Anne’s Hospital, Brno, Czech Republic  \n16HES-SO-HEG Geneva, Geneva, Switzerland  \n17SIB Swiss Institute of Bioinformatics, Geneva, Switzerland 18Dept. of Biosciences, University of Milan, Italy  \n19Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Bari, Italy.  \n20 Department of Information Engineering, University of Padova, Italy  \n21 Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy  \nKeywords: machine learning, standards, transparency, reproducibility  \nAbstract  \nSupervised machine learning ( ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME Registry, a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, promoting transparency and reproducibility of ML in the life sciences. The DOME Registry can be freely accessed from URL: [registry.dome-ml.org](registry.dome-ml.org).  \nIntroduction  \nThanks to the sharp decline in cost for many high-throughput technologies, large volumes of biological data are being generated at a rapid pace and made accessible to researchers. In this context, the field of Machine Learning ( ML) or Artificial Intelligence (AI) has risen to prominence given its applicability in data","cbCair4PY37HgrjI","https://ap.wps.com/l/cbCair4PY37HgrjI","pdf",788631,1,18,"English","en",105,"# Introduction\n## Rationale for supervised ML recommendations in biology\n# DOME recommendations and goals\n## Standards for data, optimization, evaluation, and interpretability\n# DOME Registry\n## Database purpose, identifiers, and DOME scoring\n## Supporting annotation workflows","[{\"question\":\"What problem do the DOME recommendations address in supervised machine learning research?\",\"answer\":\"They aim to improve validation and reproducibility by ensuring supervised ML studies in biology report key methodological details transparently. The recommendations address shortcomings that can hinder reliable adoption and replication.\"},{\"question\":\"What is the DOME Registry and what does it help scientists do?\",\"answer\":\"The DOME Registry is a database that lets scientists manage and access comprehensive DOME-related information about published ML studies. It streamlines annotation and organizes DOME-compliant reporting.\"},{\"question\":\"How does the DOME Registry support standardized evaluation of ML methods?\",\"answer\":\"It assigns unique identifiers and DOME scores to publications, enabling a consistent, structured assessment of supervised ML research. Unique scoring fosters comparability across methods and studies.\"}]","DOME Registry - Implementing community-wide recommendations for reporting supervised machine learning in biology | PDF",1785936362,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"dome-registry-implementing-community-wide-recommendations-for-reporting-supervised-machine-learning-in-biology","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/dome-registry-implementing-community-wide-recommendations-for-reporting-supervised-machine-learning-in-biology/127020/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem do the DOME recommendations address in supervised machine learning research?","Question",{"text":75,"@type":76},"They aim to improve validation and reproducibility by ensuring supervised ML studies in biology report key methodological details transparently. The recommendations address shortcomings that can hinder reliable adoption and replication.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the DOME Registry and what does it help scientists do?",{"text":80,"@type":76},"The DOME Registry is a database that lets scientists manage and access comprehensive DOME-related information about published ML studies. It streamlines annotation and organizes DOME-compliant reporting.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the DOME Registry support standardized evaluation of ML methods?",{"text":84,"@type":76},"It assigns unique identifiers and DOME scores to publications, enabling a consistent, structured assessment of supervised ML research. 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