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.




No vacancies currently.


Artificial Intelligence by CWI and Amsterdam UMC proposes the best radiation treatment plans in clinical practice for the first time

Artificial Intelligence by CWI and Amsterdam UMC proposes the best radiation treatment plans in clinical practice for the first time

CWI and Amsterdam UMC have developed software that helps radiation oncologists to make internal radiation treatment plans for prostate cancer. Today, the first patient was treated with a plan made with the help of this new AI technique.

Artificial Intelligence by CWI and Amsterdam UMC proposes the best radiation treatment plans in clinical practice for the first time - Read More…

Current events

Life Sciences and Health Seminar Georgios Andreadis, Solon Pissis

  • 2022-01-25T16:00:00+01:00
  • 2022-01-25T17:00:00+01:00
January 25 Tuesday

Start: 2022-01-25 16:00:00+01:00 End: 2022-01-25 17:00:00+01:00


Join Zoom Meeting

Meeting ID: 815 3524 9101
Passcode: 967201

Title:      Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images
Speaker:    Georgios Andreadis
Abstract:   Transferring information between images with large anatomical differences is an open challenge in medical image analysis. Existing methods require extensive up-front parameter tuning and have difficulty capturing large deformations and content mismatches. Using a multi-objective optimization approach with a dual-dynamic grid transformation model has previously proven effective at overcoming these issues while also producing a diverse set of high-quality registrations for 2D images. We successfully introduce the first method for multi-objective deformable image registration for 3D images, based on a new 3D dual-dynamic grid transformation model and using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) parallelized on a GPU. Our proof-of-concept prototype shows promising results on a synthetic and clinical registration problem. This talk is based on a conference paper to be published at the SPIE’22 Medical Imaging - Image Processing conference.

Title:      Differentially Private String Sanitization for Frequency-Based Mining Tasks
Speaker:    Solon P. Pissis
Abstract:   Strings are used to model genomic, natural language, and web activity data, and are thus often shared broadly. However, string data sharing has raised privacy concerns stemming from the fact that knowledge of length-k substrings of a string and their frequencies (multiplicities) may be sufficient to uniquely reconstruct the string; and from that the inference of such substrings may leak confidential information. We thus introduce the problem of protecting length-k substrings of a single string S by applying Differential Privacy (DP) while maximizing data utility for frequency-based mining tasks. Our theoretical and empirical evidence suggests that classic DP mechanisms are not suitable to address the problem. In response, we employ the order-k de Bruijn graph G of S and propose a sampling-based mechanism for enforcing DP on G. We consider the task of enforcing DP on G using our mechanism while preserving the normalized edge multiplicities in G. We define an optimization problem on integer edge weights that is central to this task and develop an algorithm based on dynamic programming to solve it exactly. We also consider two variants of this problem with real edge weights. By relaxing the constraint of integer edge weights, we are able to develop linear-time exact algorithms for these variants, which we use as stepping stones towards effective heuristics. An extensive experimental evaluation using real-world large-scale strings (in the order of billions of letters) shows that our heuristics are efficient and produce near-optimal solutions which preserve data utility for frequency-based mining tasks.

This talk is based on a conference paper presented at ICDM 2021 (joint work with Huiping Chen, Changyu Dong, Liyue Fan, Grigorios Loukides, and Leen Stougie).


Associated Members



Current projects with external funding

  • Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
  • Algorithms for PAngenome Computational Analysis (ALPACA)
  • Fast, accurate, and insightful brachytherapy treatment planning for cervical cancer through artificial intelligence (Brachytherapy treatment)
  • Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability (DAEDALUS)
  • Evolutionary eXplainable Artificial Medical INtelligence Engine (EXAMINE)
  • Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)
  • 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)
  • Networks
  • Networks COFUND postdocs
  • Uitlegbare kunstmatige intelligentie (None)
  • Optimization for and with Machine Learning (OPTIMAL)
  • Pan-genome Graph Algorithms and Data Integration (PANGAIA)
  • Transparent, Reliable and Unbiased Smart Tool for AI (TRUST-AI)

Related partners

  • AMC Medical Research
  • CNRS
  • Elekta Limited
  • European Molecular Biology Laboratory
  • Universita di Pisa
  • Xomnia
  • Academisch Medisch Centrum
  • Biomedical Imaging Group Rotterdam
  • Erasmus Universiteit Rotterdam
  • Geneton S.R.O.
  • Heinrich-Heine-Universitaet Dusseldorf
  • Illumina Cambridge
  • Institut Pasteur
  • Leids Universitair Mediach Centrum
  • Univerzita Komenskeho V Bratislave
  • Universiteit Leiden
  • Universitaet Bielefeld
  • Universita' Degli Studi di Milano-Bicocca
  • Universiteit van Tilburg