Geneversity aggregates million cell data from hundreds of scientific experiments into a single database by organizing them according to a common reference standard and nomenclature. Such a huge library makes it possible to quickly and intuitively explore the relationships between gene expression and sex, age, tissue type, cell type, disease, treatment in different class of patients.

The scientific knowledge base trapped in Geneversity can be used to train deep learning algorithms to dissect the complexity of human biology by leveraging a multimodal approach that aims to understand the molecular pathways underlying diseases under different points of view. The human body is an articulated machine that still hides many secrets. The bet of such an approach is to unlock the mysteries of modern biology by allowing an artificial intelligence to find solutions to multiple simple and complex assigned tasks.

Among the simplest tasks, for example, we can find the ability to identify cell type from the gene expression of the single cell (single-cell analysis), or the identification of cell portions starting from the gene expression of the whole tissue (bulk analysis). Anyway, among the most ambitious goals, the amazing opportunity to employ the knowledge of Geneversity to research new biomarkers predictive of diagnosis and treatment response.