AANG Holdings Group Member
Our cutting-edge technological foundation is designed for interoperability, scalability, and security. Our platforms leverage international data standards such as HL7, FHIR, LOINC, and SNOMED to ensure seamless integration across healthcare, research, and biobanking systems.
We employ modular multi-layer architectures, including Data, Service, Application, and Security Layers; with enhanced by RESTful APIs for smooth data exchange and FHIR-based pipelines for advanced data harmonization.
The systems operate on robust Linux-based infrastructures, with databases powered by MySQL and MariaDB, and incorporate fully homomorphic encryption for uncompromised data security.
Our development environment integrates Node.js, Angular, and Azure Health Data Services, while Jenkins-based CI/CD pipelines ensure continuous delivery and scalability. This technology stack creates a future-proof platform that bridges diverse systems, enabling real-time, secure, and standardized data flow across the entire biosample usage chain.
Metabio is developing an intelligent framework for assessing the quality and usability of biospecimens. The research focuses on integrating pre-analytical metadata with adaptive algorithms that can estimate sample suitability for different downstream analyses.
The project explores how a continuously evolving system can improve reproducibility, strengthen data reliability, and maximize the scientific value of each biospecimen across its lifecycle.
More broadly, this work reflects Metabio’s commitment to advancing data-driven tools that strengthen biobanking practices, foster collaboration, and support the translation of biomedical research into meaningful healthcare applications.
Research is underway to design an AI-enabled care ecosystem that adapts to the needs of individuals with intellectual disabilities, their families, and professionals. The focus is on supporting autonomy, empowerment, and social participation, while also reducing the pressures faced by caregivers and ensuring continuity of care across health, education, and social services.
The work explores innovative digital approaches that can enhance daily living, provide personalized support, and strengthen collaboration across care networks.
This initiative reflects a commitment to advancing inclusive, data-driven solutions that improve quality of life, foster independence, and promote active participation in society.
Ongoing research is exploring AI- and ML-driven approaches to support maternal, fetal, and child healthcare from conception through early childhood. The work focuses on integrating longitudinal health data across these stages to enable more informed decisions, improve risk assessment, and enhance overall care for families.
The research investigates how advanced analytics can combine clinical records, diagnostic measurements, and patient-reported information to provide real-time, interpretable insights into maternal and child health. By capturing and linking health trajectories over time, the approach aims to identify early indicators of potential complications and optimize preventive and personalized interventions.
This initiative reflects a commitment to developing intelligent, data-driven solutions that support continuity of care, promote healthier outcomes, and contribute to the understanding of long-term developmental and clinical pathways.
Artificial intelligence offers new opportunities for advancing the understanding and management of metastatic lung cancer. By analyzing large and diverse datasets—including imaging results and clinical information—AI models can uncover patterns that may not be visible through traditional analysis alone. These insights could one day support more accurate predictions about disease progression and help inform treatment strategies, contributing to ongoing efforts to improve outcomes for patients with advanced lung cancer.
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