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
CWI develops unique method for genome-reconstruction of mutated viruses
Jasmijn Baaijens, PhD student in CWI’s Life Sciences and Health group, has developed a new computational tool that can reconstruct genomes of mutated versions of viruses like HIV, Zika and Ebola
Humies Silver award for Peter Bosman and colleagues
CWI researchers Peter Bosman and Hoang Luong, along with colleagues from Amsterdam UMC, have been awarded the Humies Silver award at the GECCO 2019 conference, for their efforts in the automated generation of treatment plans for prostate cancer using brachytherapy.
Three Veni grants for CWI researchers
The Dutch Research Council (NWO) has awarded a Veni grant to Georgios Amanatidis, Marleen Balvert and Christian Majenz. The grant provides them with the opportunity to further elaborate their research ideas during a period of three years.
KWF grant for Peter Bosman together with Amsterdam UMC
Peter Bosman of CWI's Life Sciences & Health group has been awarded a project by the Dutch Cancer Society (KWF) together with the Oncology department of Amsterdam UMC.
Current events
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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Lopez Rincon, A, Martinez-Archundia, M, Martinez-Ruiz, G.U, Schönhuth, A, & Tonda, A. (2019). Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection. BMC Bioinformatics, 20(1). doi:10.1186/s12859-019-3050-8
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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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Dushatskiy, A, Alderliesten, T, Mendrik, A, & Bosman, P.A.N. (2019). Convolutional neural network surrogate-assisted GOMEA. In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 753–761). doi:10.1145/3321707.3321760
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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
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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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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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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Technische Universiteit Delft
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Universiteit Leiden