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
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.
ESTROElekta Brachytherapy Award for Anton Bouter
Anton Bouter of CWI's Life Sciences & Health group has been awarded the ESTROElekta Brachytherapy Award at the European SocieTy for Radiation & Oncology (ESTRO 38) conference in Milan, Italy.
Current events
Inaugural speech Peter Bosman
 20190906T15:00:00+02:00
 20190906T15:45:00+02:00
Inaugural speech Peter Bosman
Start: 20190906 15:00:00+02:00 End: 20190906 15:45:00+02:00
Following my appointment as parttime professor of Evolutionary Algorithms at the department of Software Technology of the faculty of Electrical Engineering, Mathematics and Computer Science of Delft University of Technology, I will give my inaugural speech on Friday, September 6, 2019 at 15:00 hours in the Aula of the Delft University of Technology.
Prior to my inaugural speech a symposium will be held, featuring the work of worldleading researchers who are active at the frontiers of the design, development, analysis, and application of evolutionary algorithms.
You are cordially invited to attend both events. However, if you plan to attend the symposium, please register HERE due to limited seating at the symposium (120 seats). No registration is required for the inaugural speech (there are sufficient seats in the Aula).
More information on the program of the day and travel information can be found HERE.
I hope to see you there!
Peter
Networks Workshop on Random graphs, counting and sampling
 20190911T13:30:00+02:00
 20190911T17:00:00+02:00
Networks Workshop on Random graphs, counting and sampling
Start: 20190911 13:30:00+02:00 End: 20190911 17:00:00+02:00
Organisers: Viresh Patel and Leen Stougie
Location: CWI, Science Park 123, 1098 XG Amsterdam, lecture room L120.
Registration is free of charge, expressing your intention to attend is highly appreciated by using the electronic registration form here.
Topic
A main theme of the workshop concerns efficient approximation algorithms for counting different types of combinatorial objects. By considering the appropriate generating functions, the problem naturally extends to that of approximately evaluating corresponding partition functions and this in turn has close connections to phase transitions in statistical physics. One approach to the sampling (and hence also to the counting) problem is through suitably defining Markov chains on the space of all the objects to be sampled and then to show that this chain mixes rapidly. This brings together ideas from combinatorics, probability, algorithms, and statistical physics. A second closely related theme is that of random graphs and their typical properties, another very active area of research in probability and discrete mathematics.
Speakers
Martin Dyer (University of Leeds, UK)
Catherine Greenhill (University of New South Wales, Sydney)
Pieter Kleer (CWI, Amsterdam)
Matteo Sfragara (University of Leiden)
Program
13:30 Matteo Sfragara
14:15 Martin Dyer
15:00 Coffee / tea
15:30 Catherine Greenhill
16:15 Pieter Kleer
17:00 Drinks
Titles and abstracts
Martin Dyer
Title: Counting independent sets in (fork,odd hole)free graphs
Abstract: Jerrum, Sinclair and Vigoda showed that the permanent of any square matrix can be estimated in polynomial time. This can be viewed as approximating the partition function of edgeweighted matchings in a bipartite graph. Equivalently, this may be viewed as approximating the partition function of vertexweighted independent sets in the line graph of a bipartite graph. Line graphs of bipartite graphs are known to be precisely the class of (claw,diamond,odd hole)free graphs. So how far does the result of Jerrum, Sinclair and Vigoda extend? We first show that it extends to all (claw,odd hole)free graphs, and then show that it extends to the even larger class of (fork,odd hole)free graphs.
(Joint work with Mark Jerrum, Haiko Müller and Kristina Vušković.)
Catherine Greenhill
Title: Approximately counting independent sets in graphs with bounded bipartite pathwidth
Abstract: In 1989, Jerrum and Sinclair showed that a natural Markov chain for counting matchings in a given graph G is rapidly mixing. This chain can equivalently be viewed as counting independent sets in line graphs. We generalise their approach to the class of all graphs with the following property: every bipartite induced subgraph of G has pathwidth at most p. Here p is a positive integer and the mixing time of the Markov chain will depend on p. We also describe two classes of graphs (described in terms of forbidden induced subgraphs) that satisfy this condition. Both of these classes generalise the class of clawfree graphs.
Pieter Kleer
Title: The switch Markov chain for generating regular graphs with a partition constraint
Abstract: The switch Markov chain is a simple procedure to randomize network topologies while preserving the degree sequence of the network. It proceeds by uniformly at random selecting two edges, and switching them if this is possible. For dregular graphs, it is known that a polynomial number of switch suffices to get an almost uniform sample from the set of all dregular graphs. In this work we consider an extension of the problem where, given some partition of the nodes into two parts, it is also specified how much edges there should be between the two parts of the partition, i.e., we are interested in dregular graphs with a partition constraint. This is a special case of the joint degree matrix problem. In this talk, we show that a polynomial number of switches suffices to get dregular graph, satisfying the partition constraint, which is close to being a uniform sample from the set of all graphs with the given partition constraint.
Title: Spectrum of Adjacency and Laplacian Matrices of Inhomogeneous ErdősRényi Random Graphs
Abstract: In homogeneous ErdősRényi random graphs G_N on N vertices in the nondense regime are considered in this talk. The edge between the pair of vertices {i, j} is retained with probability ε_N f(i/N , j/N), 1≤i=j≤N, independently of other edges, where f: [0,1]x[0,1] → [0,∞) is a continuous function such that f(x,y) = f(y,x) for all x, y ∈ [0,1]. We study the empirical distribution of the adjacency matrix A_N associated with G_N in the limit as N → ∞ when lim_(N→∞) ε_N= 0 and lim_(N→∞) Nε_N=∞. In particular, it is shown that the empirical spectral distribution of A_N, after appropriate scaling and centering, converge to deterministic limits weakly in probability. For the special case where f(x, y) = r(x)r(y) with r: [0,1]→[0,∞) a continuous function, we give an explicit characterization of the limiting distribution. Furthermore, applications of the results to ChungLu random graphs and social networks are shown.
Members
Associated Members
Publications

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:10.1007/9783030250270_15

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

Pirpinia, K, Bosman, P.A.N, Sonke, J.J, van Herk, M, & Alderliesten, T. (2019). Evolutionary machine learning for multiobjective class solutions in medical deformable image registration. Special Issue: Evolutionary Algorithms in Health Technologies, 12(5). doi:10.3390/a12050099

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

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:10.1088/20571976/aaffe7

Balvert, M, den Hertog, D, & Hoffmann, A.L. (2019). Robust optimization of dosevolume metrics for prostate HDRbrachytherapy incorporating target and OAR volume delineation uncertainties. INFORMS Journal on Computing, 31(1), 100–114. doi:10.1287/ijoc.2018.0815

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:10.1007/9783030197599_5

Pirpinia, K, Bosman, P.A.N, Loo, C.E, Russell, N.S, van Herk, M, & Alderliesten, T. (2018). Simplexbased navigation tool for a posteriori selection of the preferred deformable image registration outcome from a set of tradeoff 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

Karamanis, M, Zambrano, D, & Bohte, S.M. (2018). Continuoustime spikebased reinforcement learning for working memory tasks. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 250–262). doi:10.1007/9783030014216_25

Dora, S, Pennartz, C, & Bohte, S.M. (2018). A deep predictive coding network for inferring hierarchical causes underlying sensory inputs. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 457–467). doi:10.1007/9783030014247_45
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

3D dose reconstruction for children with longterm followup Toward improved decision making in radiation treatment for children with cancer

Enhancing proteindrug binding prediction

ICT based Innovations in the Battle against Cancer – Next  Generation Patient Tailored Brachytherapy Cancer Treatment Planning

Statistical Models for Structural Genetic Variants in the Genome of the Netherlands

Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)

MultiObjective Deformable Image Registration (MODIR) – An Innovative Synergy of MultiObjective Optimization, Machine Learning, and Biomechanical Modeling for the Registration of Medical Images with (MODIR)

Networks
Related partners

AMC Medical Research

Elekta Limited

Nucletron Operations BV

Xomnia

Academisch Medisch Centrum

Biomedical Imaging Group Rotterdam

Erasmus Universiteit Rotterdam

KiKa

Universiteit Leiden