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.
News
PhD student Lisanne Rens wins H. D. Landahl Mathematical Biophysics Award
CWI PhD student Lisanne Rens has been awarded the 2017 H. D. Landahl Mathematical Biophysics Award. Every two years, the Society for Mathematical Biology gives out this reward in recognition of a scientist who is making outstanding scientific contributions to the advancement of mathematical biology during their doctoral studies.
Cells’ own contractions stimulate growth orientation
During the growth of embryos cells align with forces from the surrounding tissue. These forces originate from differences in growth rates, muscle contractions or gravity. Mathematicians at the CWI research centre in Amsterdam now showed for the first time that active cell contractions can accelerate and facilitate this reorientation of cells. Their research was recently published in the leading Biophysical Journal (112).
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
Members
Associated Members
Publications
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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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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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Baller, A.C, van Ee, M, Hoogeboom, M, & Stougie, L. (2019). Complexity of inventory routing problems when routing is easy. Networks. doi:10.1002/net.21908
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Abedin, P, Ganguly, A, Pissis, S, & Thankachan, S.V. (2019). Range Shortest Unique Substring queries. In Proceedings of the International Symposium on String Processing and Information Retrieval (pp. 258–266). doi:10.1007/978-3-030-32686-9_18
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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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Janssen, R, Jones, M.E.L, Kelk, S.M, Stamoulis, G, & Wu, T. (2019). Treewidth of display graphs: bounds, brambles and applications. Journal of Graph Algorithms and Applications, 23(4), 715–743. doi:10.7155/jgaa.00508
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Barton, C, Kociumaka, T, Liu, C, Pissis, S, & 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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Murakami, Y, van Iersel, L.J.J, Janssen, R, Jones, M.E.L, & Moulton, V. (2019). Reconstructing tree-child networks from reticulate-edge-deleted subnetworks. Bulletin of Mathematical Biology, 81, 3823–3863. doi:10.1007/s11538-019-00641-w
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Charalampopoulos, P, Kociumaka, T, Pissis, S, Radoszewski, J, Rytter, W, Straszyński, J, … Zuba, W. (2019). Circular pattern matching with k mismatches. In Proceedings of Fundamentals of Computation Theory (pp. 213–228). doi:10.1007/978-3-030-25027-0_15
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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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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Deep Learning based genome wide association techniques for uncovering the missing heritability and novel, clinically actionable genetic variants in ALS (DEEP GWAS for ALS)
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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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Pan-genome Graph Algorithms and Data Integration (PANGAIA)
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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Geneton S.R.O.
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Illumina Cambridge
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Institut Pasteur
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Stichting ALS
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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