About Us ISGlobal & BRGE Research Team

Meet the team behind MediNet - combining medical expertise with AI technology to improve healthcare collaboration through federated learning.

About ISGlobal & BRGE

MediNet is developed by the Bioinformatic Research Group in Epidemiology (BRGE) at ISGlobal, an institute for global health research based in Barcelona.

Our group specializes in developing statistical methods and computational tools for omic and exposome data, with expertise in genomic inversions, genetic mosaicisms, and data protection. We've created multiple open-source R packages in Bioconductor and focus on precision medicine approaches.

MediNet represents our commitment to enabling secure healthcare collaboration through federated learning, combining our expertise in bioinformatics, epidemiology, and data privacy.

9 Researchers at ISGlobal
10+ R Packages Developed
100% Open Source
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Bioinformatics Research

Focused on genomic analysis, statistical methods, and computational biology for medical applications.

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Data Protection

Experience in data privacy, genetic data protection, and secure computational methods for healthcare.

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Global Health Focus

Contributing to ISGlobal's mission to improve health through research and technological innovation.

Learn more about our research:

link BRGE Research Group public ISGlobal Institute

Meet Our Team

The team behind MediNet

Dr. Juan R. González

Dr. Juan R. González

Principal Investigator & Group Leader

Associate Research Professor at ISGlobal and Assistant Professor at UAB. Leads the BRGE group with expertise in statistical methods for omic data, exposomics, and genomic analysis. Researcher in bioinformatics and epidemiology with multiple publications and R packages.

Genomic Analysis Bioinformatics R/Bioconductor Exposomics
Ramon Mateo

Ramon Mateo

Research Assistant & PhD Student in Bioinformatics

Research assistant at BRGE-ISGlobal and PhD student in bioinformatics specializing in federated learning applications for healthcare. Currently developing MediNet platform as part of doctoral research, focusing on privacy-preserving machine learning methods and secure collaborative AI solutions for medical institutions.

Federated Learning Bioinformatics Healthcare AI PhD Research

Our Mission

We believe that medical AI should be built collaboratively while preserving patient privacy. Our experience in both healthcare and technology drives us to create solutions that are not only technically advanced but also practical for real-world medical environments.

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Privacy First

Patient data never leaves the hospital - only model improvements are shared

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Medical Expertise

Built by professionals who understand both healthcare needs and technical challenges

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Collaboration

Enabling hospitals to work together while maintaining complete data sovereignty