Description
Leader of the group Life Sciences and Health: Leen Stougie.
Our research group creates fundamental knowledge and applied solutions in the broad field of life sciences. We promote understanding of how biological processes work in detail. Our interdisciplinary team of computer scientists, mathematicians and theoretical biologists develops new models, theories, and decision support systems in collaboration with experimental biologists and medical experts. We are motivated by applications of our work in practice
Watch our group video to get a glimpse of our activities or click here for more information about our group structure.
Vacancies
Postdoctoral researchers on evolutionary algorithms, machine learning, and biomechanical modelling to innovate automated registration of medical images
CWI closely collaborates with the Academic Medical Center (AMC) in Amsterdam to work on innovations in the medical domain along the entire spectrum from algorithmic foundations to clinical integration. Currently, CWI and AMC have a joint project for which we seek multiple talented postdocs to work on novel combinations of multi-objective evolutionary algorithms, machine learning, and biomechanical modelling for deformable image registration
PhD students on evolutionary algorithms, machine learning, and biomechanical modelling to innovate automated registration of medical images
CWI closely collaborates with the Academic Medical Center (AMC) in Amsterdam to work on innovations in the medical domain along the entire spectrum from algorithmic foundations to clinical integration. Currently, CWI and AMC have a joint project for which we seek multiple talented PhD students to work on novel combinations of multi-objective evolutionary algorithms, machine learning, and biomechanical modelling for deformable image registration.
Tenure Track position in Mathematics and Computer Science for Life Sciences and Health
The CWI Life Sciences and Health (LSH) group is a group of computer scientists and mathematicians who have their research focus on the analysis and design of models and algorithms and their direct application to important challenges in the LSH domain. Our present research team of computer scientists and mathematicians has expertise in e.g. computational genomics, medical informatics, computational phylogenetics and biological network analysis. We come from diverse methodological backgrounds, such as computational intelligence, computational data science and operations research. Methodologically, we develop new theories, models, algorithms and decision support tools, mostly for problems arising in collaboration with experimental biologists and medical experts. We also actively collaborate in projects with academic hospitals, biological and biochemical research institutes and industry.
News
New network model can learn to remember past events
Scientists understand how neurons, the smallest computational units of the brain, behave during tasks, but how brains learn to make efficient choices is unknown, in particular when the brain’s working memory is involved.
CWI to improve ICT for radiotherapy
CWI launches a research project with the Academic Medical Center (AMC) and the company Elekta Brachytherapy to improve the ICT that is used in radiotherapy for cancer patients. In the next years, researchers of these organizations will investigate the improvement of medical software and instruments that are used for internal radiation.
New mathematical models predict success rate of drug trials
A large part of drug trials fail when it turns out that the results for laboratory animals do not transfer to humans (1). New mathematical models developed at Centrum Wiskunde & Informatica (CWI) are able to predict the transferability of results between different organisms.
Mohammed El-Kebir receives BioSB Young Investigator Award
Mohammed El-Kebir of CWI’s Life Sciences group has been awarded the BioSB Young Investigator Award for best PhD thesis in the field of bioinformatics and systems biology in the Netherlands.
Current events
LSH Seminar Ziyuan Wang
- 2018-10-09T15:00:00+02:00
- 2018-10-09T16:00:00+02:00
LSH Seminar Marco Virgolin
- 2018-09-25T15:00:00+02:00
- 2018-09-25T16:00:00+02:00
LSH Seminar Leen Stougie
- 2018-09-11T15:00:00+02:00
- 2018-09-11T16:00:00+02:00
LSH Seminar Joke Blom
- 2018-08-28T15:00:00+02:00
- 2018-08-28T16:00:00+02:00
LSH Seminar Alexander Schoenhuth
- 2018-07-03T14:00:00+02:00
- 2018-07-03T15:00:00+02:00
LSH Seminar Alexander Schoenhuth
Start: 2018-07-03 14:00:00+02:00 End: 2018-07-03 15:00:00+02:00
TBA
LSH Seminar Marjolein van der Meer
- 2018-06-19T15:00:00+02:00
- 2018-06-19T16:00:00+02:00
LSH Seminar Marjolein van der Meer
Start: 2018-06-19 15:00:00+02:00 End: 2018-06-19 16:00:00+02:00
TBA
Members
Associated Members
Publications
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Bosman, T, van Ee, M, Jiao, Y, Marchetti Spaccamela, A, Ravi, R, & Stougie, L. (2018). Approximation algorithms for replenishment problems with fixed turnover times. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 217–230). doi:10.1007/978-3-319-77404-6_17
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Feuerstein, E, Marchetti Spaccamela, A, Schalekamp, F, Sitters, R.A, van der Ster, S.L, Stougie, L, & van Zuylen, A. (2017). Minimizing worst-case and average-case makespan over scenarios. Journal of Scheduling, 20(6), 545–555. doi:10.1007/s10951-016-0484-y
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Zambrano, D, Nusselder, R.B.P, Scholte, H.S, & Bohte, S.M. (2017). Efficient computation in adaptive artificial spiking neural networks. arXiv.org e-Print archive.
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Engler, M.S, Klau, G.W, van Dijk, M, Geerke, D.P, Veen, L.E, Ridder, L, … Mark, A.E. (2017). Graph-based molecular topology generation - An NLeSC-ASDI eScience project for enhancing protein-drug binding prediction.
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Szabó, A, & Merks, R.M.H. (2017). Blood vessel tortuosity selects against evolution of aggressive cells in confined tissue environments: A modeling approach. PLoS Computational Biology, 13(7). doi:10.1371/journal.pcbi.1005635
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Bouter, A, Luong, N.H, Witteveen, C, Alderliesten, T, & Bosman, P.A.N. (2017). The multi-objective real-valued gene-pool optimal mixing evolutionary algorithm. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 537–544). doi:10.1145/3071178.3071274
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Luong, N.H, Bouter, A, van der Meer, M.C, Niatsetski, Y, Witteveen, C, Bel, A, … Bosman, P.A.N. (2017). Efficient, effective, and insightful tackling of the high-dose-rate brachytherapy treatment planning problem for prostate cancer using evolutionary multi-objective optimization algorithms. In GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1372–1379). doi:10.1145/3067695.3082491
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Sadowski, K.L, van der Meer, M.C, Luong, N.H, Alderliesten, T, Thierens, D, van der Laarse, R, … Bosman, P.A.N. (2017). Exploring trade-offs between target coverage, healthy tissue sparing, and the placement of catheters in HDR brachytherapy for prostate cancer using a novel multi-objective model-based mixed-integer evolutionary algorithm. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 1224–1231). doi:10.1145/3071178.3071311
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Thierens, D, & Bosman, P.A.N. (2017). Model-based evolutionary algorithms. In GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 545–575). doi:10.1145/2908961.2926975
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Bouter, A, Witteveen, C, Alderliesten, T, & Bosman, P.A.N. (2017). Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 705–712). doi:10.1145/3071178.3071272
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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3D dose reconstruction for children with long-term follow-up Toward improved decision making in radiation treatment for children with cancer
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Enhancing protein-drug binding prediction
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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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Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
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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)
Related partners
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Elekta Limited
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Nucletron Operations BV
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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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KiKa
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Technische Universiteit Delft
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Universiteit Leiden