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Chilam is a recent initiative, led by the C3 – Centro de Ciencias de la Complejidad of the Universidad Nacional Autónoma de Mexico, that unites several existing research projects that share both a common purpose – to apply a big-data and a machine learning-based approach to problems of high social impact – as well as a common theoretical framework – Bayesian machine-learning prediction models.
The first and most developed project is the SPECIES Platform, developed in collaboration with the Consejo Nacional de la Biodiversidad (CONABIO) since 2013 and based on a novel, innovative approach to spatial modelling and aimed towards modelling biodiversity patterns and the ecological aspects of emerging diseases.
Project 42, developed since 2014, on the other hand, is directed at providing a unique, highly multifactorial data set for the study of obesity and metabolic diseases and a Bayesian modelling platform for the Chilam projects involve over 80 researchers and students from multiple disciplines across the exact, life and social sciences.
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Proyectos
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Research project that identifies and analyzes the causal relationships between factors that influence human behavior linked to obesity with the aim of building a mental model, which encompasses concepts such as beliefs, perception of oneself and the world.
EPI-SPECIES
Infectious diseases (ID) represent one of the main threats to public health worldwide. Globally more than 70% of IDs originated from zoonotic pathogens circulating in wildlife and domestic animals. Zoonoses are generated by a wide spectrum of microorganisms including: bacteria, viruses, fungi, protozoa and helminths. Many factors can establish and accelerate the origin of zoonoses. […]
Project 42
In spite of a huge investment in both human and financial capital, the incidence of obesity and metabolic diseases, such as type 2 diabetes, is increasing worldwide. Although such diseases are rightly considered to be “complex”, the implications for how they should be studied and analyzed have not been fully appreciated. Their complexity is manifest […]
EpI-PUMA
In short, a user will be able to quickly create their own predictive model focused on a prediction linked to a decision in a friendly environment where no specialized knowledge, neither modeling nor machine learning is required.
SPECIES
SPECIES is an interactive tool for analyzing ecological niches and the potential distribution of species. Furthermore, it can be used to infer networks between species that can highlight potential interaction between species. SPECIES harbors information of 2 to 3 billion biological organisms distributed worldwide. This information represents a large source to understand biodiversity and its […]
Miembros
Para Chilam, la colaboración y conocimiento transdisciplinario es fundamental para abordar los enormes retos que supone el análisis y desarrollo de los sistemas complejos, es por eso que integra el talento de diversas entidades e instituciones académicas y gubernamentales, organizaciones y empresas, como miembros activos y colaboradores.