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Welcome to the CBIO website

The Centre for Computational Biology is a Common Research Centre of MINES ParisTech, one of the major engineering schools for applied mathematics in France, and ARMINES, a private non-profit research and technological organisation. Our research is dedicated to the development of methods and tools in the fields of Machine Learning, Statistics and Computer Vision in order to analyze massive data generated in life sciences and medicine. We work on a broad range of applications, from questions in fundamental life science to precision medicine. The Centre for Computational Biology has a partnership with Inserm, the French national institute of health and medical research, and Institut Curie, a major hospital and research center dedicated to cancer. This partnership provides access to the infrastructure and facilities of the Institut Curie and facilitates collaborative projects with other groups at the Institut Curie, as well as data sharing. Our laboratory is located in the heart of Paris and we therefore benefit from an exceptional scientific and cultural environment.

Keywords: computational biology, bioinformatics, machine learning, statistics, precision medicine, bioimaging, bioimage informatics, computer vision, virtual screening, chemoinformatics, genetics, systems biology, big data

Key publications
  • O. Collier, V. Stoven, J.-P. Vert. A single- and multi-task machine learning algorithm for the prediction of cancer driver genes. PLoS Computational Biology (in press).
  • H. Climente-González, C.-A. Azencott, S. Kaski, M. Yamada. Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data. Bioinformatics, 2019, 35(14) i427–i435, (ISMB/ECCB Proceedings). [link]
  • L. Slim, C. Chatelain, C.-A. Azencott, J.-P. Vert. kernelPSI: a post-selection inference framework for nonlinear variable selection, Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML), 2019 97:5857—5865. [link]
  • P. Naylor, M. Laë, F. Reyal and T. Walter. Segmentation of Nuclei in Histopathology Images by deep regression of the distance map. IEEE Transactions on Medical Imaging, 2018. [link]
  • A. Samacoits, R. Chouaib, A. Safieddine, A.-M. Traboulsi, W. Ouyang, C. Zimmer, M. Peter, E. Bertrand, T. Walter and F. Mueller. A computational framework to study sub-cellular RNA localization. Nature Communications, 9(1), 4584, 2018. [link]
  • B. Playe, C.-A. Azencott and V. Stoven. Efficient multi-task chemogenomics for drug specificity prediction. PLoS ONE 13(10), 2018. [link]
  • N. Tsanov, A. Samacoits, R. Chouaib, A.-M. Traboulsi, T. Gostan, C. Weber, C. Zimmer, K. Zibara, T. Walter, M. Peter, E. Bertrand and F. Mueller. smiFISH and FISH-quant - a flexible single RNA detection approach with super-resolution capability. Nucleic Acids Research, September 2016. [link]
  • T. Traoré, A. Cavagnino, N. Saettel, F. Radvanyi, S. Piguel, I. Bernard-Pierrot, V. Stoven, M. Legraverend. New aminopyrimidine derivatives as inhibitors of the TAM family. European Journal of Medicinal Chemistry, 70:789-801, 2013 [link]