Getting a holistic view of biological systems and their context to understand their complexity and provide new insights and applications that can benefit human and environmental health.
We focus on Multimodal Data, processing and analyzing diverse types such as mass spectrometry (proteomics, metabolomics) and metaomics (metagenomics, metatranscriptomics, metaproteomics), tackling complex biological problems.
A key component is developing High-quality Knowledge Graphs that connect data, allowing integration and interpretation of these data and we use Graph Machine Learning to extract insights, revealing patterns and generating predictions from graph structures.
We apply these technologies to explore and understand Microbial Communities and their environments, unraveling assembly, interaction, adaptation, and impact, spanning ecology to biotech.
Further, we’re strong advocates of Open Science, adhering to open principles for our generated data, training, and software, aiming to reduce inequality and promote accessibility by democratizing data science.
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