iMODELER
An advanced platform that integrates supervised machine learning and algorithmic reasoning for the predictive analysis of cellular flows and complex biomedical simulations.
The research problem
Traditional methods for microbiota analysis can involve long turnaround times, high costs and considerable operational complexity.
These limits make them harder to use in clinical settings that require fast, automatable and scalable analysis.
Objectives
- Fast and affordable microbiota analysis.
- Automatic identification of microorganisms.
- Development of a scalable and automatable solution.
- Applicability of the technology in real clinical settings.
- Representation of information through graph structures.
Results
The platform integrates single-cell imaging, supervised neural networks and graph modelling.
The architecture also allows the approaches developed to be reused in domains other than medicine, such as cybersecurity, industry, smart cities, recommender systems, knowledge graphs, finance and HR analytics.
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Our researchers and engineers provide computational audit sessions, licensing of proprietary algorithms and joint co-development projects for companies and research institutions.