[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125574-en":3,"doc-seo-125574-105":30,"detail-sidebar-cat-0-en-105":87},{"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},125574,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","SIBILA - A Novel Interpretable Ensemble of General-Purpose Machine Learning Models Applied to Medical Contexts","Personalized medicine remains a major challenge as machine learning and deep learning promise more suitable therapy for individual patients. Yet widespread adoption is hindered by the need to build custom models for each dataset, the limited interpretability of their outputs, and the high computational cost of explanation. SIBILA is presented as an interpretable ensemble that integrates multiple attribution algorithms and uses consensus to produce global feature attribution. It is containerized for portable execution on local systems or HPC platforms, and can be used as a free web server for non-expert users. The approach is validated through medical classification case studies and additional supplementary tasks.","Graphical Abstract  \nSIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts  \nAntonio Jess Banegas-Luna, Horacio Prez-Snchez  \nHighlights  \nSIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts  \nAntonio Jess Banegas-Luna, Horacio Prez-Snchez  \n• Machine learning is a powerful approach for the analysis of medical data.  \n• Black-box models are not useful in critical contexts, such as medicine. Interpretability is therefore a must.  \n• SIBILA has been developed as an ensemble of ML models which can be easily trained, evaluated and explained in a simple workflow.  \n• As interpretability is very computationally expensive, it has been accelerated by means of HPC. SIBILA can be run either locally or on HPC platforms.  \n• To deal with differently configured HPC infrastructures, SIBILA has been containerized with Singularity. This makes of SIBILA a very portable tool.  \nSIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to  \nmedical contexts  \nAntonio Jess Banegas-Lunaa, Horacio Prez-Sncheza  \naStructural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Catlica de Murcia (UCAM), Campus de los Jernimos, Murcia, 30107, Murcia, Spain  \nAbstract  \nPersonalized medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made them a feasible alternative for predicting the most appropriate therapy for individual patients. However, the need to develop a custom model for every dataset, the lack of interpretation of their results and high computational requirements make many reluctant to use these methods.  \nAiming to save time and bring light to the way models work internally, SIBILA has been developed. SIBILA is an ensemble of machine learning and deep learning models that applies a range of interpretability algorithms to identify the most relevant input features. Since the interpretability algorithms may not be in line with each other, a consensus stage has been implemented to estimate the global attribution of each variable to the predictions. SIBILA is containerized to be run on any high-performance computing platform. Although conceived as a command-line tool, it is also available to all users free of charge as a web server at [https://bio-hpc.ucam.edu/sibila](https://bio-hpc.ucam.edu/sibila). Thus, even users with few technological skills can take advantage of it.  \nSIBILA has been applied to two medical case studies to show its ability to predict in classification problems. Even though it is a general-purpose tool, it has been developed with the aim of becoming a powerful decision-making tool for clinicians, but can actually be used in many other domains. Thus, other two non-medical examples are supplied as supplementary material to  \nprove that SIBILA still works well with noise and in regression problems. Keywords:  \nFebruary 16, 2023  \ndeep learning, high-performance computing, explainable artificial intelligence, personalized medicine, consensus, decision-making  \n1. Introduction  \nThe rapid development of technologies has helped artificial intelligence (AI) become a well-known and reliable tool for researchers in academia and industry. Its ability to analyze vast amounts of data has become a powerful tool in science and business. Looking for repetitive patterns among such datasets is a complex but necessary task that needs to be done to extract knowledge from past events. Once the rules managing raw data are identified, AI models can use them to make predictions about new unexplored samples.  \nMachine learning (ML) and, its subtype, Deep learning (DL) are two typical approaches of AI [1] . Both types of models are flexible enough to analyze a range of datasets, including tabular data, time series and images. This adaptability to different contexts has propelled their application into traditional and ","cbCaifzRfDOHCLvn","https://ap.wps.com/l/cbCaifzRfDOHCLvn","pdf",730095,1,23,"English","en",105,"# Highlights\n# Introduction\n# Graphical Abstract\n# Abstract\n# Keywords","[{\"question\":\"How can SIBILA be executed for different computing environments?\",\"answer\":\"SIBILA is containerized with Singularity, enabling portable runs either locally or on HPC platforms, and it is also available as a free web server.\"},{\"question\":\"What kind of validations and applications are shown for SIBILA?\",\"answer\":\"It is applied to two medical case studies for classification tasks, and supplementary non-medical examples demonstrate robustness to noise and performance in regression problems.\"}]","SIBILA - A Novel Interpretable Ensemble of General-Purpose Machine Learning Models Applied to Medical Contexts | PDF",1785899965,58,{"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":82,"head_meta":84,"extra_data":86,"updated_unix":28},"sibila-a-novel-interpretable-ensemble-of-general-purpose-machine-learning-models-applied-to-medical-contexts","",{"@graph":36,"@context":81},[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/sibila-a-novel-interpretable-ensemble-of-general-purpose-machine-learning-models-applied-to-medical-contexts/125574/",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],{"name":72,"@type":73,"acceptedAnswer":74},"How can SIBILA be executed for different computing environments?","Question",{"text":75,"@type":76},"SIBILA is containerized with Singularity, enabling portable runs either locally or on HPC platforms, and it is also available as a free web server.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kind of validations and applications are shown for SIBILA?",{"text":80,"@type":76},"It is applied to two medical case studies for classification tasks, and supplementary non-medical examples demonstrate robustness to noise and performance in regression problems.","https://schema.org",{"og:url":52,"og:type":83,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":85,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":88},[89,93,97,101,106,111,116,119,124,127,131],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Exam",70,"exam",{"id":102,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},5,"Comic",60,"comic",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},6,"Technology",50,"technology",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},30,"research-report",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},9,"Religion & Spirituality",20,"religion-spirituality",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":122,"slug":126},"World Cup","world-cup",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":128,"slug":130},10,"Lifestyle","lifestyle",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":102,"slug":134},19,"General","general"]