Chaire Tremplin de l’Institut Prairie
computational biology, genomics, machine learning
23 September 2021
, updated on
2 December 2021
Institut de biologie de l’ENS (IBENS)
46 rue d’Ulm Paris 75005
CNRS research associate working at the interphase of machine-learning and genomics. I develop machine-learning tools for the analysis and integration of multi-omic bulk and single-cell data.
Field of research
This is a crucial moment for the study and clinical management of complex diseases. Single-cell sequencing introduced a paradigm shift in healthcare, from precision medicine to cell-based interceptive medicine. Indeed single-cell multi-modal data ( i.e. large-scale quantitative measurements such as transcriptome, epigenome, spatial positioning at the resolution of single cells) provide us the potential (i) to track the molecular trajectories performed by human cells during disease onset or therapy resistance; (ii) to provide a mechanistic understanding of the complex networks that drive the different phases of the transformation and (iii) to delineate the contribution of neighboring cells in such transformation. However, to make these biological objectives a reality we need to develop powerful methods able to co-analyze multi-modal single-cell data and extract actionable biological knowledge.
My research activity lies at the interphase of machine learning and genomics. I design machine learning methods able to co-analyze and translate the enormous amount of available single-cell multi-modal data into actionable biological knowledge. I then apply the developed methods in collaboration with wet-lab biologists to derive concrete biological hypothesis and ultimately contribute to improve the understanding and clinical management of complex diseases.
- Kang, Y., Thieffry, D., & Cantini, L. (2021). Evaluating the reproducibility of single-cell gene regulatory network inference algorithms. Frontiers in genetics, 12, 362.
- Cantini, L., Zakeri, P., Hernandez, C., Naldi, A., Thieffry, D., Remy, E., & Baudot, A. (2021) Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nature Communications 12, 124.
- Cantini L, Kairov U., de Reyniès A., Barillot E., Radvanyi F., Zinovyev A., (2019) Assessing reproducibility of matrix factorization methods in independent transcriptomes, Bioinformatics, 35,21.
- Cantini, L. Isella C., Petti C., Picco G., Chiola S., Ficarra E., Caselle M., Medico E. (2015). MicroRNA-mRNA interactions underlying colorectal cancer molecular subtypes. Nature Communications, 6.
- Cantini, L., Bertoli, G., Cava, C., Dubois, T., Zinovyev, A., Caselle, M., ... & Martignetti, L. (2019). Identification of microRNA clusters cooperatively acting on epithelial to mesenchymal transition in triple negative breast cancer. Nucleic acids research, 47(5), 2205-2215.
- Greco, A., Sanchez-Valle, J., Pancaldi, V., Baudot, A., Barillot, E., Caselle, M., ... & Cantini, L. (2019). Molecular Inverse Comorbidity between Alzheimer's disease and Lung Cancer: new insights from Matrix Factorization. Int. J. Mol. Sci., 20(13), 3114