Single-cell high-throughput sequencing, a major breakthrough in life sciences, allows to access the integrated molecular profiles of thousands of cells in a single experiment. This abundance of data provides tremendous power to unveil unknown cellular mechanisms. However, single-cell data are so massive and complex that it has become challenging to give clues to their underlying biological processes.
The machine learning for integrative genomics G5 group works at the interface of machine learning and genomics, developing methods exploiting the full richness and complementarity of the available single-cell data to derive actionable biological knowledge.
All tools developed by the team are available at https://github.com/cantinilab
