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.
News
Shape instructs future fate of cells in Nature Communications
How does a cell make a decision about his future, on what type of cell it will be after the cell division? This process is described by Roeland Merks and Cong Chen of the Centrum Wiskunde & Informatica (CWI) in Amsterdam in an article in Nature Communications that was published on 28 June, together with a Japanese team of biologists.
ERCIM News 104 on Big Data in Life Sciences co-coordinated by Roeland Merks
ERCIM News No. 104 was published in January 2016 at http://ercim-news.ercim.eu/en104
CWI develops new techniques for studying vascular growth
Blood vessels are essential for efficient transport of oxygen and nutrients throughout the body, and play a crucial role in tumor growth and wound healing. Sonja Boas, PhD student in CWI's Life Sciences group, developed new techniques to study vascular growth computationally.
Current events
PhD Defence Jasmijn Baaijens (LSH)
- 2019-09-25T10:30:00+02:00
- 2019-09-25T11:30:00+02:00
PhD Defence Jasmijn Baaijens (LSH)
Start: 2019-09-25 10:30:00+02:00 End: 2019-09-25 11:30:00+02:00
Everyone is invited to attend the public defence of Jasmijn of her PhD thesis: De novo approaches to haplotype-aware genome assembly.
Promotor: prof.dr. Alexander Schönhuth
- Email: Jasmijn Baaijens
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iCal
Seminar by Martin Farach-Colton, Better File Systems through Data Structures
- 2019-10-16T16:00:00+02:00
- 2019-10-16T17:00:00+02:00
Seminar by Martin Farach-Colton, Better File Systems through Data Structures
Start: 2019-10-16 16:00:00+02:00 End: 2019-10-16 17:00:00+02:00
Martin Farach-Colton (Rutgers University) will be visiting CWI on Wednesday October 16. He will be giving a theory/practice talk which may be of your interest.
Title: Better File Systems through Data Structures
Abstract: This talk will describe the lessons learned from the BetrFS project, a research file system based on write-optimized data structures. BetrFS outperforms other file systems by orders of magnitude on their worst cases, while matching them on their best cases. BetrFS is also immune to file-system aging (i.e. fragmentation), a problem that can cause up to 20x slowdowns in other file systems. The talk will explain what write-optimized data structures are, how they work, and how they are used in BetrFS.
- Email: Solon Pissis
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iCal
Members
Associated Members
Publications
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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
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Barton, C, Kociumaka, T, Liu, C, Pissis, S.P, & Radoszewski, J. (2019). Indexing weighted sequences: Neat and efficient. Information and Computation. doi:10.1016/j.ic.2019.104462
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Charalampopoulos, P, Kociumaka, T, Pissis, S.P, Radoszewski, J, Rytter, W, Straszyński, J, … Zuba, W. (2019). Circular pattern matching with k mismatches. In Fundamentals of Computation Theory (pp. 213–228). doi:10.1007/978-3-030-25027-0_15
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Yin, B, Balvert, M, van der Spek, R.A.A, Dutilh, B.E, Bohte, S.M, Veldink, J, & Schönhuth, A. (2019). Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype. In Bioinformatics (Vol. 35, pp. i538–i547). doi:10.1093/bioinformatics/btz369
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Pirpinia, K, Bosman, P.A.N, Sonke, J.-J, van Herk, M, & Alderliesten, T. (2019). Evolutionary machine learning for multi-objective class solutions in medical deformable image registration. Special Issue: Evolutionary Algorithms in Health Technologies, 12(5). doi:10.3390/a12050099
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Wang, Z, Virgolin, M, Bosman, P.A.N, Balgobind, B.V, Bel, A, & Alderliesten, T. (2019). Automatic radiotherapy plan emulation for 3D dose reconstruction to enable big data analysis for historically treated patients. In Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications. doi:10.1117/12.2512758
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Kelly, M, van Amerongen, J.H.M, Balvert, M, & Craft, D. (2019). Dynamic fluence map sequencing using piecewise linear leaf position functions. Biomedical Physics & Engineering Express, 5(2). doi:10.1088/2057-1976/aaffe7
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Balvert, M, den Hertog, D, & Hoffmann, A.L. (2019). Robust optimization of dose-volume metrics for prostate HDR-brachytherapy incorporating target and OAR volume delineation uncertainties. INFORMS Journal on Computing, 31(1), 100–114. doi:10.1287/ijoc.2018.0815
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Neumann, F, Polyakovskiy, S, Skutella, M, Stougie, L, & Wu, J. (2019). A Fully Polynomial Time Approximation Scheme for Packing While Traveling. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-19759-9_5
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Pirpinia, K, Bosman, P.A.N, Loo, C.E, Russell, N.S, van Herk, M, & Alderliesten, T. (2018). Simplex-based navigation tool for a posteriori selection of the preferred deformable image registration outcome from a set of trade-off solutions obtained with multiobjective optimization for the case of breast MRI. Journal of Medical Imaging, 5(4). doi:10.1117/1.JMI.5.4.045501
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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Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
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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
Related partners
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AMC Medical Research
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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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Universiteit Leiden