Langevin, Maxime

A Python library for probabilistic analysis of single-cell omics data

Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 789

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

Lopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M. I., & Yosef, N. (2019) · arXiv

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 105

Machine learning yield prediction from NiCOlit, a small-size literature data set of nickel catalyzed C–O couplings

Schleinitz, J., Langevin, M., Smail, Y., Wehnert, B., Grimaud, L., & Vuilleumier, R. (2022) · Journal of the American Chemical Society

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 88