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Pasteur Erasmus 2018 2019 Projects 2018-2019 Institut Pasteur 2018-2019 RESEARCH CENTRE Legal name: Institut Pasteur Address: 25-28 rue du Dr. Roux, 75724 Cedex 15, PARIS Country: France Website: https://www.pasteur.fr/education Contact person: Elisabetta Bianchi, David Itier, Sylvie Malot Contact person e-mail: [email protected] Brief description of your Institution The Institut Pasteur is a private non-profit foundation that contributes to the prevention and treatment of diseases through research, education, and public health activities. Its campus in Paris hosts almost 2600 individuals. Research: priority is given to fight infectious diseases, such as viral, bacterial, and parasitic diseases, as well as certain types of cancer, genetic, neurodegenerative, and allergic diseases. Education: every year 550 young scientists from all over the world follow high-level courses in various fields related to research in microbiology, immunology, cellular biology, epidemiology, genetics, and disease control. Over 850 trainees from 60 different countries come to perfect their skills or conduct their Master or Doctoral trainings in the Institute's laboratories. Description of the work program(s) See projects on following pages N° of placements available for work programs a), b), c) etc: The laboratories at Institut Pasteur, Paris, France have proposed 34 projects for Erasmus internships (see following pages). In addition, two projects have been presented from the International Pasteur Network, and are mainly reserved for PhD level students to be discussed by the candidates with their University). Students may also contact other laboratories at Pasteur to apply for an internship, even if the laboratories have not presented a project. 2 Institut Pasteur 2018-2019 FACILITIES (not compulsory for the host centre) at Institut Pasteur, Paris • Accommodation (some centres offer it) X YES 1 NO a limited number of rooms for rent are reserved for Pasteur at the student residence Cité Universitaire http://www.ciup.fr/ • Support in finding accommodation (many centres offer it) X YES 1 NO • Canteen (most centres offer it) X YES 1 NO • Additional salary X YES 1 NO Institut Pasteur Paris offers an additional salary of approximately 550 euros/month, which is paid by the host lab (3.75 euros/hour, 7 hours/working day). NOTE: internship conditions at the International Pasteur Network may vary and have to be discussed directly with the host lab. 3 Institut Pasteur 2018-2019 Title of the work program 1 Deep learning for medical diagnosis of brain tumors Description of the work program Brain tumors are among the most devastating forms of cancer, and extracting diagnostic information from the tumor accurately and rapidly is key to inform therapeutic decision making. Currently, this process requires labor-intensive inspection of very large histology images by trained pathologists and suffers from relatively high error rates and large differences in accuracy between local and reference pathologists. Deep learning methods use artificial neural networks to learn relationships between complex numerical data sets and underlie the current renaissance in artificial intelligence (AI)1. In recent years, deep learning methods have achieved remarkable success in image classification, including for medical applications such as dermatology, radiology or ophthalmology2,3. In collaboration with a team of neuropathologists led by F. Chrétien (Institut Pasteur and Hôpital Sainte Anne, Paris), we seek to explore the potential of deep learning methods to automate and improve the classification of tumors based on histological images. Using training data obtained by our collaborators, a previous project has led to the development of a deep learning algorithm that classifies incoming histological images into one of three types of tumors (ependymoma, glioma grade III, glioma grade IV). According to our quantitative evaluations, the algorithm currently achieves a classification accuracy of ~80%, slightly lower than the accuracy of a local pathologist (85%). The main goal of this project is to improve the classification accuracy to the level of a reference pathologist (95%). To this end, we propose to explore multiple avenues, including the following: (i) The current neural network is pretrained on general imaging data (from Imagenet4). We will test pretraining on more similar histological data from the Cancer Genome Atlas project, which should yield better learning performance. (ii) The current classification scheme operates on 300 small image patches extracted from each histology slide and uses a majority voting procedure for final classification. This is certainly suboptimal, we therefore propose to test changes to the voting procedure, for example by automatically learning how to weigh results from different patches for better overall classification and by incorporating patch-wise classification uncertainty. (iii) We will use multiresolution decompositions of the image to test the predictive power of large-scale cellular patterns and leverage access to an Nvidia DGX GPU station to increase the size of input images. (iv) We will take into account human diagnostic uncertainty (as determined based on an expert consensus) to adjust the weights of distinct training data In addition to improving classification accuracy, another goal is to determine the image patterns that are most informative for accurate cancer diagnosis, using techniques to probe the sensitivity of individual neurons in the trained neural network5. 4 Institut Pasteur 2018-2019 We expect that this project will result in an improved AI-based tool for the diagnosis, and ultimately treatment, of brain cancer. References: 1. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). 2. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). 3. Kermany, D. S. et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell 172, 1122–1131.e9 (2018). 4. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. in Advances in Neural Information Processing Systems 1097–1105 (2012). 5. Zeiler, M. & Fergus R. Visualizing and Understanding Convolutional Networks. Lect. Notes Comput. Sci. 8689, 818–833 (2014). Tutor/supervisor First name, Last name Christophe, Zimmer Phone E-mail [email protected] Profile on https://www.researchgate.net/profile/Christophe_Zimme http://www.researchgate.net r / (if applicable): Lab websites: https://sites.google.com/site/imagingandmodeling/ https://research.pasteur.fr/en/team/imaging-and-modeling/ Selected publications or patents of the Research Group offering the work program A. Aristov, B. Lelandais, E. Rensen, C. Zimmer. ZOLA allows flexible 3D localization microscopy over an adjustable axial range. Nature Communications, 8:2409 (2018). doi: 10.1038/s41467-018-04709-4 W. Ouyang, A. Aristov, M. Lelek, X. Hao, C. Zimmer. Deep learning massively accelerates super-resolution localization microscopy. Nature Biotechnology, 36 (5), 460–468, 2018. doi:10.1038/nbt.4106. S. Herbert, A. Brion, J.-M. Arbona, M. Lelek, A. Veillet, B. Lelandais, J. Parmar, F. Fernandez, E. Alamyrac, Y. Khalil, E. Birgy, E. Fabre, and C. Zimmer. Chromatin stiffening underlies enhanced locus mobility after DNA damage in budding yeast EMBO Journal. Sep 1; 36(17):2595-2608 (2017). doi: 10.15252/embj.201695842 Arbona J-M, S. Herbert, E. Fabre, C. Zimmer. Inferring the physical properties of yeast chromatin through Bayesian analysis of whole nucleus simulations. Genome Biology. 18:81, doi: 10.1186/s13059-017-1199-x (2017). 5 Institut Pasteur 2018-2019 Scientific or technical background required for work program We expect a background in computer science, physics, mathematics, or a related field, and good programming skills. Prior training or experience in machine learning and ability to communicate with biologists and clinicians are a big plus. 6 Institut Pasteur 2018-2019 Title of the work program 2 Double-strand break repair of trinucleotide repeats: from mechanisms to gene therapy Description of the work program Trinucleotide repeat expansions are involved in a number of neurodegenerative disorders, including Huntington disease, myotonic dystrophy type 1 (DM1 or Steinert disease) and several ataxias. Expansion mechanisms include DNA slippage during replication, homologous recombination and DNA repair. It was recently shown that break-induced replication – a specific type of homologous recombination – was a major driver of trinucleotide repeat expansions (reviewed in [1]). Given that the pathology is always associated to the expansion of one single trinucleotide repeat tract, shortening this expanded repeat to non-pathological length should suppress clinical symptoms, and could therefore be theoretically used as a gene therapy approach [2]. We recently showed that a TALEN designed to recognize and cut a CTG triplet repeat from a DM1 patient, integrated into a yeast chromosome, was very efficient at shortening the repeat (>99% cells showed contraction) and highly specific too, since no other mutation was detected in yeast cells in which the TALEN was induced [3]. On the contrary, similar experiments were performed with a CRISPR-Cas9 nuclease and showed a reduced efficacy and a lack of specificity leading to large chromosomal rearrangements (our own unpublished results). Hence, it appears that TALENs and CRISPR-Cas nucleases behave differently on the same DNA substrate, but the reason
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