Young PI (G5) in Institut Pasteur and CNRS permanent researcher. Her research activity is focused on the design of machine learning methods for the integration of single-cell multi-modal data. Mathematician by training, Laura received her PhD in cancer systems biology from the University of Turin (Italy). She then pursued a postdoc in the cancer system biology group at Institut Curie (Paris). In 2018, awarded the L’Oréal-UNESCO for Women in Science and EMBO fellowship, she joined CSAIL at MIT (USA), before being selected as CNRS permanent researcher. She is recipient of the ERC StG 2023, ANR JCJC 2020, Sanofi iTech Awards 2020 and L’Oréal-UNESCO for Women in Science fellow (2018 edition).
Topics of interest
Single-cell omics data, multi-modal integration, network inference
Project in Prairie
Laura Cantini will develop computational methods for multi-modal single-cell data integration. She will in particular combine multi-omics joint dimensionality reduction, to identify the cell types and states present in a biological sample, and network-based methods to reconstruct the multi-omics regulatory mechanisms underlying each cell type/state. Finally, by applying the developed approaches to patient-derived data, she will contribute to improve our understanding of cancer heterogeneity and its underlying molecular mechanisms.
The timely detection and successful treatment of cancer depends on our ability to understand when, why, and how a subpopulation of cells deviates away from a healthy state or acquires drug resistance. Single-cell multi-modal data, produced at increasing peace, offer the opportunity to tackle these questions. The current major bottleneck is the crucial need for computational methods able to translate this wealth of information into actionable biological knowledge.