The large and heterogeneous datasets that characterize Data-Intensive Domains (DID) 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.

The CoMoDID (Combining Explainable Artificial Intelligene and Conceptual Modeling for Data-Intensive Domains Management) 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, transitioning from conceptual models to implementation through model transformations. Additionally, a platform is being designed and developed to support the efficient integration and analysis of data.

From a technological standpoint, 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—ensuring that every analytical result can be traced, justified, and trusted.

The social and healthcare impact is especially strong. By applying the DELFOS method in the genomic medicine 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 gender perspective, 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.

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—and by extension, the Valencian Community—as leaders in digital health innovation, contributing to the competitiveness of the regional and national R&D ecosystem.

Finally, the project aligns with European strategic goals, including those outlined in Horizon Europe (especially Pillar 1 and Pillar 2 – Digital and Health clusters), and initiatives like TAILOR and NESSI, 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

The primary application domain is genomics, focusing on predicting critical diseases before symptoms appear. By integrating genomic data with AI-driven insights, the research advances precision medicine, 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.

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