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
Doctoral Student Position Available on Computational Pangenomics
PhD student, on the subject of Computational Pan-genomics: Algorithms & Applications.
News
Alexander Schoenhuth appointed Professor Genome Data Science
Starting 1 October 2017, Alexander Schoenhuth of Centrum Wiskunde & Informatica (CWI) in Amsterdam is appointed Professor ‘Genome Data Science’ at the Faculty of Science of Utrecht University. It concerns a part-time appointment for one day a week.
CWI PhD student Jasmijn Baaijens wins Best Talk Award at the ISMB-HitSeq conference
Jasmijn Baaijens, PhD student from CWI’s Life Sciences and Health group, has won the Best Talk Award at ISMB-HitSeq, the world's leading conference on high-throughput genomics.
Instable blood supply may help healthy cells compete with tumor cells
Researchers of CWI’s Scientific Computing group have found that instabilities in the blood supply in cancerous tissue can, surprisingly, lead to a less favorable environment for tumor cells. Their findings shed light on the potential negative side effects of current treatments that aim to actually normalize the blood supply in cancerous tissues.
Comparing medical images better
Together with the radiation oncology department of AMC, Peter Bosman of CWI’s Life Sciences and Health group has been awarded 1.4 million euros for a project in NWO’s Open Technology Programme for the research and development of a new medical image registration method. This project is co-funded by companies Elekta and Xomnia.
Current events
PhD Defense Stef Maree
- 2021-03-17T14:00:00+01:00
- 2021-03-17T15:00:00+01:00
PhD Defense Stef Maree
Start: 2021-03-17 14:00:00+01:00 End: 2021-03-17 15:00:00+01:00
Everyone is invited to attend the public defense of Stef of his PhD thesis:
Model-based evolutionary algorithms for finding diverse high-quality solutions - with an application in brachytherapy for prostate cancer
Promotor 1: Prof.dr. Peter A.N. Bosman, CWI, Amsterdam / TU Delft
Promotor 2: Prof.dr. C.R.N. Rasch, AMC / UvA
Copromotor 1: Dr. Tanja Alderliesten, LUMC, Leiden / UvA
Copromotor 2: Dr. A. Bel, AMC / UvA
- Email: Stef Maree
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iCal
Members
Associated Members
Publications
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Jones, M.E.L, Kelk, S.M, & Stougie, L. (2021). Maximum parsimony distance on phylogenetic trees: A linear kernel and constant factor approximation algorithm. Journal of Computer and System Sciences, 117, 165–181. doi:10.1016/j.jcss.2020.10.003
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Charalampopoulos, P, Kociumaka, T, Pissis, S, Radoszewski, J, Rytter, W, Straszyński, J, … Zuba, W. (2021). Circular pattern matching with k mismatches. Journal of Computer and System Sciences, 115, 73–85. doi:10.1016/j.jcss.2020.07.003
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Amir, A, Charalampopoulos, P, Pissis, S, & Radoszewski, J. (2020). Dynamic and Internal Longest Common Substring. Algorithmica, 82(12), 3707–3743. doi:10.1007/s00453-020-00744-0
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Maree, S.C, Alderliesten, T, & Bosman, P.A.N. (2020). Ensuring smoothly navigable approximation sets by Bézier curve parameterizations in evolutionary bi-objective optimization. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-58115-2_15
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Deist, T.M, Maree, S.C, Alderliesten, T, & Bosman, P.A.N. (2020). Multi-objective optimization by uncrowded hypervolume gradient ascent. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-58115-2_13
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Ayad, L.A.K, Dourou, A.-M, Arhondakis, S, & Pissis, S. (2020). IsoXpressor: A Tool to Assess Transcriptional Activity within Isochores. Genome biology and evolution, 12(9), 1573–1578. doi:10.1093/gbe/evaa171
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Alamro, H, Alzamel, M, Iliopoulos, C.S, Pissis, S, Sung, W.-K, & Watts, S. (2020). Efficient Identification of k -Closed Strings. International Journal of Foundations of Computer Science, 31(5), 595–610. doi:10.1142/S0129054120500288
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Thierens, D, & Bosman, P.A.N. (2020). Model-based evolutionary algorithms: GECCO 2020 tutorial. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 590–619). doi:10.1145/3377929.3389868
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Virgolin, M, Wang, Z, Alderliesten, T, & Bosman, P.A.N. (2020). Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction. Journal of Medical Imaging, 7(4). doi:10.1117/1.JMI.7.4.046501
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Bouter, P.A, Maree, S.C, Alderliesten, T, & Bosman, P.A.N. (2020). Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm. In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 603–611). doi:10.1145/3377930.3390225
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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Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability (DAEDALUS)
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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