Abstract
Data related to human health, from clinical and epidemiological information to medical images and omics data, are being generated and accumulated in an unprecedented amount in history. The analysis and integration of such information is critical to improve our understanding of the pathophysiology of diseases, their transmission, as well as their diagnosis and treatment. This project proposes innovative approaches for: analyzing large epidemiological databases; mapping hotspots of disease transmission; integrating transcriptome data with clinical and immunological data; and using machine learning for interpretation and analysis of microscopic images. State-of-the-art techniques or systems analyses and machine learning will be used and even developed for each approach, taking into account the foundations of integrative biology. We hope that the results of the project can clarify several problems related to human health, from the automatic analysis of microscopic slides to the molecular mechanisms of infectious and inflammatory diseases (AU)
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