{"id":6148,"date":"2025-01-15T17:17:35","date_gmt":"2025-01-15T16:17:35","guid":{"rendered":"https:\/\/pros.webs.upv.es\/site\/?page_id=6148"},"modified":"2025-06-23T10:43:12","modified_gmt":"2025-06-23T09:43:12","slug":"comodid","status":"publish","type":"page","link":"https:\/\/pros.webs.upv.es\/site\/projects\/comodid\/","title":{"rendered":"CoMoDID: Combining Explainable Artificial Intelligence and Conceptual Modelling for Data Intensive Domains Management"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row full_width=&#8221;stretch_row_content&#8221; gap=&#8221;10&#8243;][vc_column width=&#8221;4\/6&#8243; css=&#8221;.vc_custom_1461679591584{margin-left: 80px !important;}&#8221;][vc_column_text css=&#8221;&#8221;]<a href=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/01\/logoComodid.png\"><img decoding=\"async\" class=\" wp-image-6150 aligncenter\" src=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/01\/logoComodid.png\" alt=\"\" width=\"324\" height=\"96\" \/><\/a><\/p>\n<p>The large and heterogeneous datasets that characterize <strong>Data-Intensive Domains (DID)<\/strong> pose significant challenges for data analysis and management. Extracting meaningful knowledge from DID-based systems requires assembling and analyzing these datasets, yet integrating diverse data sources remains a complex and arduous task.<\/p>\n<p>The <strong>CoMoDID (<\/strong><i><strong>Combining Explainable Artificial Intelligene and Conceptual Modeling for Data-Intensive Domains Management<\/strong><\/i><strong>) <\/strong>project addresses this challenge by leveraging foundational ontologies and conceptual modeling (CM) in the software development process of DID-based systems. The primary objective is to define a structured method for building such systems, <strong>transitioning from conceptual models to implementation through model transformations<\/strong>. Additionally, a platform is being designed and developed to support the efficient integration and analysis of data.<\/p>\n<p data-start=\"718\" data-end=\"1290\">From a <strong data-start=\"725\" data-end=\"753\">technological standpoint<\/strong>, the project will deliver a functional platform capable of integrating, analyzing, and explaining genomic data. The platform will serve as a proof-of-concept of how combining CM and XAI can improve system quality and usability. It is designed to be generalizable to other data-intensive domains such as e-commerce or climatology. A particularly valuable innovation is the incorporation of explainability mechanisms into the entire lifecycle of data processing\u2014ensuring that every analytical result can be traced, justified, and trusted.<\/p>\n<p data-start=\"1292\" data-end=\"1833\">The <strong data-start=\"1296\" data-end=\"1328\">social and healthcare impact<\/strong> is especially strong. By applying the DELFOS method in the <strong data-start=\"1388\" data-end=\"1408\">genomic medicine<\/strong> field, CoMoDID has the potential to improve the early detection and understanding of genetic diseases, aiding in the development of more effective and personalized treatments. The project also explicitly includes a <strong data-start=\"1624\" data-end=\"1646\">gender perspective<\/strong>, ensuring that genomic differences between men and women are considered when designing and validating solutions. This helps advance equity in biomedical research and health technologies.<\/p>\n<p data-start=\"1835\" data-end=\"2350\">Economically, the project strengthens the connection between academic research and industry. By collaborating with hospitals, biotech companies, and SMEs, CoMoDID fosters knowledge transfer and the development of tools that can be commercialized or integrated into healthcare IT infrastructures. Furthermore, it positions the participating institutions\u2014and by extension, the Valencian Community\u2014as leaders in digital health innovation, contributing to the competitiveness of the regional and national R&amp;D ecosystem.<\/p>\n<p data-start=\"2352\" data-end=\"2822\">Finally, the project aligns with European strategic goals, including those outlined in <strong data-start=\"2439\" data-end=\"2457\">Horizon Europe<\/strong> (especially Pillar 1 and Pillar 2 \u2013 Digital and Health clusters), and initiatives like <strong data-start=\"2545\" data-end=\"2555\">TAILOR<\/strong> and <strong data-start=\"2560\" data-end=\"2569\">NESSI<\/strong>, which emphasize trustworthy AI and responsible digital transformation. Its results will be disseminated through top-tier conferences, journals, workshops, and industrial outreach events, maximizing visibility and fostering international collaboration<\/p>\n<p>The primary application domain is <strong>genomics<\/strong>, focusing on predicting critical diseases before symptoms appear. By integrating genomic data with AI-driven insights, the research advances<strong> precision medicine<\/strong>, enabling early diagnosis and personalized treatments. ML identifies disease presence, while XAI ensures interpretability, fostering trust in AI-driven medical predictions. A multidisciplinary team, spanning multiple European research centers, brings expertise in DID systems, AI, and genomics to drive this innovation forward.<\/p>\n<p style=\"text-align: right;\"><a href=\"http:\/\/www.pros.upv.es\/projects\/\">Go Back to\u00a0Projects<\/a><\/p>\n<p>[\/vc_column_text][vc_column_text]<br \/>\n[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/6&#8243; css=&#8221;.vc_custom_1737717012519{margin-top: 80px !important;margin-left: 100px !important;background-color: #A09E9E !important;border-radius: 4px !important;}&#8221;][vc_column_text css=&#8221;&#8221;]<b>Main Researcher:<\/b><br \/>\n\u00d3scar Pastor L\u00f3pez<\/p>\n<p><b>Period: <\/b>sept.2022 &#8211; dic.2025<\/p>\n<p><b>Reference:<br \/>\n<\/b><span class=\"TextRun SCXW118832130 BCX0\" lang=\"ES-ES\" xml:lang=\"ES-ES\" data-contrast=\"auto\"><span class=\"NormalTextRun CommentHighlightRest SCXW118832130 BCX0\">CIPROM\/2021\/023<\/span><\/span><\/p>\n<p><b>Funding Organization:<\/b><br \/>\nGeneralitat Valenciana (Programa PROMETEO para grupos de investigaci\u00f3n de excelencia)<\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-2939 size-full\" src=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2015\/09\/uni.jpg\" alt=\"uni\" width=\"176\" height=\"76\" srcset=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2015\/09\/uni.jpg 176w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2015\/09\/uni-50x22.jpg 50w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2015\/09\/uni-150x65.jpg 150w\" sizes=\"(max-width: 176px) 100vw, 176px\" \/><\/p>\n<p><a href=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/gv_distintiu_horitzontal_cmyk_val-scaled.jpg\"><img decoding=\"async\" class=\"alignnone wp-image-6194\" src=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/gv_distintiu_horitzontal_cmyk_val-300x176.jpg\" alt=\"\" width=\"189\" height=\"111\" srcset=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/gv_distintiu_horitzontal_cmyk_val-300x176.jpg 300w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/gv_distintiu_horitzontal_cmyk_val-1024x600.jpg 1024w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/gv_distintiu_horitzontal_cmyk_val-1536x901.jpg 1536w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/gv_distintiu_horitzontal_cmyk_val-scaled.jpg 2048w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/gv_distintiu_horitzontal_cmyk_val-300x176@2x.jpg 600w\" sizes=\"(max-width: 189px) 100vw, 189px\" \/><\/a><\/p>\n<p><a href=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/logo_gva.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-6192\" src=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/logo_gva-300x178.png\" alt=\"\" width=\"177\" height=\"105\" srcset=\"https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/logo_gva-300x178.png 300w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/logo_gva-1024x609.png 1024w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/logo_gva.png 1188w, https:\/\/pros.webs.upv.es\/site\/wp-content\/uploads\/2025\/06\/logo_gva-300x178@2x.png 600w\" sizes=\"(max-width: 177px) 100vw, 177px\" \/><\/a>[\/vc_column_text][\/vc_column][vc_column width=&#8221;1\/6&#8243;][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row full_width=&#8221;stretch_row_content&#8221; gap=&#8221;10&#8243;][vc_column width=&#8221;4\/6&#8243; css=&#8221;.vc_custom_1461679591584{margin-left: 80px !important;}&#8221;][vc_column_text css=&#8221;&#8221;] The large and heterogeneous datasets that characterize Data-Intensive Domains (DID) pose significant [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":2914,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"plain-container","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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Intensive Domains Management - PROS - Research Center on Software Production Methods","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/pros.webs.upv.es\/site\/projects\/comodid\/","og_locale":"en_GB","og_type":"article","og_title":"CoMoDID: Combining Explainable Artificial Intelligence and Conceptual Modelling for Data Intensive Domains Management - PROS - Research Center on Software Production Methods","og_description":"[vc_row full_width=&#8221;stretch_row_content&#8221; gap=&#8221;10&#8243;][vc_column width=&#8221;4\/6&#8243; css=&#8221;.vc_custom_1461679591584{margin-left: 80px !important;}&#8221;][vc_column_text css=&#8221;&#8221;] The large and heterogeneous datasets that characterize Data-Intensive Domains (DID) pose significant [&hellip;]","og_url":"https:\/\/pros.webs.upv.es\/site\/projects\/comodid\/","og_site_name":"PROS - Research Center 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