Artificial Intelligence Laboratory proposes solutions in biology, medicine and pharmacology

Development of new machine learning techniques, based on 'graph theory' and statistics, with the aim of offering answers to many challenges in the areas of computational biology, medicine and pharmacology, through the discovery of patterns and regularities in large amounts of data, as well as the integration of information from different sources.

This is the central proposal for the creation of the Artificial Intelligence Laboratory – aimed at the health area – by the School of Applied Mathematics at Fundação Getúlio Vargas (EMAp/FGV), for whom Artificial Intelligence is already “a reality in people’s lives”. people". The institution understands that “innovations in this area, until recently restricted to industry or activities technologies, now directly impact society, through the discovery of new applications and tools".

Led by Professor Alberto Paccanaro, an international authority on machine learning applied to Biology and Medicine, the PaccanaroLab, as if calls its laboratory, has the participation of young researchers and students from several countries, such as the United Kingdom, France, Paraguay, in addition to Brazil.

In addition, all the laboratory's work has the collaboration of experimental biologists and clinical scientists, responsible for validating the models developed by the researchers. Through this collaborative network, feedbacks on applications, to improve the so-called 'predictive powers'.

“Much of our research focuses on the development of new mathematical methods especially suitable for making inferences in biological networks, based on the most recent results of the learning of machine. In particular, the methods we develop take into account both the structure of the networks that represent the data, as well as the structure of the network that represents the biological question to be answered”, explains Paccanaro.

Check out some lab studies here:

• Prediction of Function of Proteins.
• Network Medicine;
• Computational Pharmacology;
• Inference of relationships between genotype, phenotype and environment;
• Inference and analysis of large-scale protein-protein interaction networks;
• Analysis of Biological Processes from coexpression networks;
• 3D Genome Analysis;

Created in 2011, the School of Applied Mathematics aims to develop contemporary mathematics, adapted to the challenges of the information and knowledge era. The School offers undergraduate courses in Applied Mathematics and in Data Science and Artificial Intelligence, in addition to courses of Graduate Studies, stimulating research and development projects, as well as internal and external institutional partnerships.