Safinianaini, N., de Souza, C. P. E., Lagergren, J., CopyMix: Mixture Model Based Single-Cell Clustering and Copy-Number Calling using Variational Inference. Preprint available at BioRxiv (

de Souza, C. P. E.*, Andronescu, M.*, Masud, T., Kabeer, F., Biele, J., Laks, E., Lai, D., Brimhall, J., Wang, B., Su, E., Hui, T., Cao, Q., Wong, M., Moksa, M., Moore, R. A., Hirst, M., Aparicio, S., Shah, S. P., Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data. Under review, preprint available at BioRxiv ( *Authors contributed equally to this work.

Safinianaini, N., de Souza, C. P. E., Bostrom, H., and Lagergren, J., Orthogonal Mixture of Hidden Markov Models. Accepted and to appear in the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2020. 

Randhawa, Gurjit S., Maximillian P.M. Soltysiak, Hadi El Roz, Camila P. E. de Souza, Kathleen A. Hill, and Lila Kari, Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLOS ONE. 

Shirazi, R., de Souza, C. P. E., Kashef, R. and Rodrigues, F. F., (2020) Deep Learning in the Healthcare Industry: Theory and Applications, Book Chapter. In Computational Intelligence and Soft Computing Applications in Healthcare Management Science (pp. 220-245). IGI Global.

Santos, E.S., de Morais Oliveira, C.D., Menezes, I.R.A., do Nascimento, E.P., Correia, D.B., de Alencar, C.D.C., de Fátima Sousa, M., Lima, C.N.F., Monteiro, Á.B., de Souza, C.P.E. and de Araújo Delmondes, G., 2019. Anti-Inflammatory Activity of herb products from Licania rigida Benth. Complementary Therapies in Medicine.

Zhang, A. W., McPherson A., Milne, K., Kroeger, D. R., Hamilton, P. T., Miranda, A., Funnell, T., Little, N., de Souza C. P. E., Laan, S., LeDoux, S., Cochrane, D. R., Lim, J. L. P., Yang, W.,  Roth, A., et al., (2018) Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell.

Farahani, H.*, de Souza, C. P. E.*, Billings, R.*, Yap, D., Shumansky, K., Wan, A., Lai, D., Mes-Masson, A-M., Aparicio, S., Shah, S. P., Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer, Nature Scientific Reports 7(1), 13467. *Authors contributed equally to this work.

de Souza, C. P. E., Heckman, N. E. and Xu, F., (2017) Switching nonparametric regression models for multi-curve data, The Canadian Journal of Statistics, 45, 442-460.


McPherson, A., Roth, A., Ha, G., Chauve, C., Steif, A., de Souza, C. P. E., Eirew, P., Bouchard-Côté, A., Aparicio, S., Sahinalp, S., and Shah, S., (2017) ReMixT: Clone Specific Genomic Structure Estimation in Cancer, Genome biology, 18(1), 140


Lenzi, A., de Souza, C. P. E., Dias, R., Garcia, N. L. and Heckman, N. E. (2017) Analysis of aggregated functional data from mixed populations with application to energy consumption, Environmetrics, 28(2)


Eirew, P., Steif, A., Jaswinder, K., Ha, G., Yap, D., Farahani, H., Gelmon, K., Chia, S., Mar, C., Wan, A., Laks, E., Biele, J., Shumansky, K., Rosner, J., McPherson, A., Nielsen, C., Roth, A., Lefebvre C., Bashashati, A., de Souza, C., Siu, C., Aniba, R., Brimhall, J., Oloumi, A., Osako, T., Bruna, A., Sandoval, J.,  Algara, T., Greenwood, W., Leung, K., Cheng, H., Xue, H., Wang, Y., Lin, D., Mungall, A., Moore, R., Zhao, Y., Lorette, J., Nguyen, L., Huntsman, D., Eaves, C. J., Hansen, C., Marra, M. A., Caldas, C., Shah, S. P., Aparicio, S. (2015)  Dynamics of genomic clones in breast cancer patient xenografts at single cell resolution, Nature 518, 422–426


De Souza, C. P. E. and Heckman, N. E. (2014) Switching nonparametric regression models, Journal of Nonparametric Statistics, 26(4), 617-637 (winner of the 2014 Journal of Nonparametric Statistics Best Student Paper Award). R package switchnpreg available.


De Souza, C. P. E. and Dias, R. (2010) Introdução à Análise de Dados Funcionais (Introduction to Functional Data Analysis). Monograph from the 19th National Symposium of Probability and Statistics (135 pages). Published by Associação Brasileira de Estatística (Brazilian Association of Statistics), São Paulo, Brazil.

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