Description
Leader of the group Life Sciences and Health: Leen Stougie.
The CWI Life Sciences and Health (LSH) group is a group of computer scientists and mathematicians whose research focus is on the analysis and design of models and algorithms as well as their direct application to important challenges in the LSH domain.
On the application side, our present team of researchers has expertise in, e.g., computational genomics, medical informatics, computational phylogenetics, and biological network analysis. On the methodological side, we come from different backgrounds, e.g., computational intelligence, computational data science, and operations research. Methodologically, we develop new theories, models, algorithms and decision support tools, for problems that arise mostly in collaboration with experimental biologists and medical experts. We actively collaborate in projects with academic hospitals, biological and biochemical research institutes, and industry. Click here for more information about our group structure.
The LSH group participates in the INRIA International team ERABLE.
Seminars: The LSH group organizes a biweekly seminar.
Watch our group video to get a glimpse of our activities.
Vacancies
PhD students on explainable AI, genetic programming & deep learning for medicine
Centrum Wiskunde & Informatica (CWI, the Dutch national research institute for mathematics and computer science) located in Amsterdam, and the Leiden University Medical Centre (LUMC) together have 3 vacancies for fully funded
News
Marie Curie ITN grant awarded to ALPACA network
Recently, CWI and others were awarded an EU Marie Skłodowska-Curie Innovative Training Networks (ITN) consortium grant for ALPACA –'Algorithms for PAngenome Computational Analysis'. The research project involves a total funding of 3.67 million euros.
CWI designs algorithms for the improvement of Genetic Programming
Marco Virgolin of CWI’s Life Sciences & Health group has researched ways to improve the efficiency and effectiveness of Genetic Programming (GP). He defends his thesis ‘Design and Application of Gene-Pool Optimal Mixing Evolutionary Algorithms for Genetic Programming’ on Monday 6 June.
Peter Bosman awarded grant of 1M euros from NWO’s Open Technology Programme
Peter Bosman’s project proposal DAEDALUS (Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability) has been granted by NWO TTW in its Open Technology Programme.
€1.5 million European grant for mathematics consortium NETWORKS
The NETWORKS consortium can now continue growing thanks to a €1.5 million grant from the European Union’s Horizon2020 programme.
Members
Associated Members
Publications
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Ray, S, Lall, S, & Bandyopadhyay, S. (2020). CODC: A Copula-based model to identify differential coexpression. Systems Biology and Applications, 6. doi:10.1038/s41540-020-0137-9
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van den Broek d'Obrenan, A, Ridder, A, Roubos, D, & Stougie, L. (2020). Minimizing bed occupancy variance by scheduling patients under uncertainty. European Journal of Operational Research, 286(1).
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Lähnemann, D, Köster, J, Szczurek, E, McCarthy, D.J, Hicks, S.C, Robinson, M.D, … Schönhuth, A. (2020). Eleven grand challenges in single-cell data science. Genome Biology, 21. doi:10.1186/s13059-020-1926-6
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Pusa, T, Galvão Ferrarini, M, Andrade, R, Mary, A, Marchetti Spaccamela, A, Stougie, L, & Sagot, M.-F. (2020). MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network. Bioinformatics, 36(2), 514–523. doi:10.1093/bioinformatics/btz584
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Bernardini, G, Chen, H, Fici, G, Loukides, G, & Pissis, S. (2020). Reverse-safe data structures for text indexing. In Proceedings of the Workshop on Algorithm Engineering and Experiments (pp. 199–213).
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Baaijens, J.A, Stougie, L, & Schönhuth, A. (2020). Strain-aware assembly of genomes from mixed samples using flow variation graphs. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-45257-5_14
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Maree, S.C, Bosman, P.A.N, van Wieringen, N, Niatsetski, Y, Pieters, B.R, Bel, A, & Alderliesten, T. (2020). Automatic bi-objective parameter tuning for inverse planning of high-dose-rate prostate brachytherapy. Physics in Medicine and Biology, 65(7). doi:10.1088/1361-6560/ab7362
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Nersisyan, L, Nikoghosyan, M, Arakelyan, A, Francioli, L.C, Menelaou, A. (Androniki), Pulit, S.L. (Sara L.), … The Genome of the Netherlands Consortium, . (2019). WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene. Nature Scientific Reports, 9(1). doi:10.1038/s41598-019-55109-7
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Bosman, T. (2019, November 26). Relax, Round, Reformulate: Near-Optimal Algorithms for Planning Problems in Network Design and Scheduling.
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Luong, N.H, Alderliesten, T, Pieters, B.R, Bel, A, Niatsetski, Y, & Bosman, P.A.N. (2019). Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy. Applied Soft Computing, 84. doi:10.1016/j.asoc.2019.105681
Software
VirtualLeaf: a modeling framework for plant tissue morphogenesis
VirtualLeaf is a computer modeling framework for the simulation of plant tissue morphogenesis, i.e., the biological development of an organism's shape
Current projects with external funding
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ICT based Innovations in the Battle against Cancer – Next - Generation Patient -Tailored Brachytherapy Cancer Treatment Planning ()
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Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion ()
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Enhancing protein-drug binding prediction
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Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
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Algorithms for PAngenome Computational Analysis (ALPACA)
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Fast, accurate, and insightful brachytherapy treatment planning for cervical cancer through artificial intelligence (Brachytherapy treatment)
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Evolutionary eXplainable Artificial Medical INtelligence Engine (EXAMINE)
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Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)
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Multi-Objective Deformable Image Registration (MODIR) – An Innovative Synergy of Multi-Objective Optimization, Machine Learning, and Biomechanical Modeling for the Registration of Medical Images with (MODIR)
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Networks
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Optimization for and with Machine Learning (OPTIMAL)
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Pan-genome Graph Algorithms and Data Integration (PANGAIA)
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Transparent, Reliable and Unbiased Smart Tool for AI (TRUST-AI)
Related partners
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AMC Medical Research
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CNRS
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Elekta Limited
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European Molecular Biology Laboratory
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INRIA
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Nucletron Operations BV
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Universita di Pisa
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Xomnia
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Academisch Medisch Centrum
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Biomedical Imaging Group Rotterdam
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Erasmus Universiteit Rotterdam
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Geneton S.R.O.
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Heinrich-Heine-Universitaet Dusseldorf
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Illumina Cambridge
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Institut Pasteur
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Leids Universitair Mediach Centrum
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Technische Universiteit Delft
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Univerzita Komenskeho V Bratislave
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Universiteit Leiden
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Universitaet Bielefeld
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Universita' Degli Studi di Milano-Bicocca
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Universiteit van Tilburg