A group of collaborators from CABANAnet and EMBL’s European Bioinformatics Institute (EMBL-EBI) have joined to address some disparities in bioscience-related AI through training. The BiotrAIn project, supported by the Chan Zuckerberg Initiative, aims to create a fundamental and sustainable curriculum on artificial intelligence (AI) for bioscientists from Latin America, including data science basics, and leading towards the use and application of AI to better solve biological and biomedical problems.
Our intended impact is that Latin America is able to participate as an equal partner in globally important bioscience projects that use or develop artificial intelligence methods.
Cath Brooksbank - EMBL-EBI, Principal Investigator, UK
Jose Arturo Molina Mora - University of Costa Rica, Co-Investigator, lead for LATAM, Costa Rica
Rebeca Campos Sáchez - University of Costa Rica, Co-Investigator, CABANAnet, Costa Rica
Kim Gurwitz - EMBL-EBI, Project manager, UK
Cindy Aguilar Bartels - University of Costa Rica, Project manager, Costa Rica
Juanita Riveros - EMBL-EBI, Events Organiser, UK
Carla Valeria Filippi, Facultad de Agronomia, Universidad de la República, Uruguay
Maria Ines Fariello, Facultad de Ingeniería, Universidad de la República, Uruguay
Maria Fernanda Dias, Federal University of Rio de Janeiro (UFRJ) – Center for Health Sciences (CCS) – Institute of Biodiversity and Sustainability (NUPEM), Brazil
Nelly Selem, National Autonomous University of México Center of Mathematical Sciences, México
Adrián Turjanski, Facultad de ciencias exactas y natirales, Universidad de Buenos Aires, Argentina
Tulio Campos, Oswaldo Cruz Foundation (Fiocruz), Brazil
By the end of this course, participants will be able to apply foundational concepts of artificial intelligence and machine learning to biological data, including situations in the LATAM context, leveraging their existing data analysis skills to:
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Identify appropriate AI concepts and techniques, ethical aspects, and general usability of AI in bioscience.
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Preprocess and analyze biological datasets using clustering algorithms, including regional databases (LATAM).
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Build and evaluate classification models to address biological questions in the LATAM context.
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Apply deep learning models, including tools such as AlphaFold, to analyze complex biological research problems.
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Deliver AI contents with best teaching/learning practices in biological research.
More details at: EMBL-EBI
This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
