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Our research focuses on the development and application of artificial intelligence methods to build computational representations of biological entities, including proteins, genes, regulatory regions, cells, and organisms. These representations transform complex biological objects into numerical spaces where they can be compared, organized, and analyzed using mathematical and computational approaches.<\/p>\n
A central goal of our work is to understand what biological information is encoded within these representations. In particular, we investigate how properties such as molecular function, evolutionary relationships, biological diversity, and the organization of cellular systems are embedded in these representation spaces, enabling the exploration of biological principles that are not readily apparent from the original data.<\/p>\n
Another major area of research involves the development of predictive tools based on these representations. These approaches enable the transfer of knowledge between related proteins, genes, or cells, overcoming some of the limitations of traditional methods that rely exclusively on sequence similarity or homology.<\/p>\n
Finally, these representations are used as tools to address fundamental questions in evolutionary and functional biology. These include the study of evolutionary convergence, the emergence of novel biological functions, the organization of protein functional landscapes, and the identification of large-scale evolutionary patterns. The ultimate goal is to use artificial intelligence not only as a predictive tool, but also as a framework for understanding how biological information is organized and evolves.<\/p>\n
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