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

No vacancies currently.

News

Current events

Life Sciences and Health Arthur Guijt, Seminar Anton Bouter

  • 2021-11-30T16:00:00+01:00
  • 2021-11-30T17:00:00+01:00
November 30 Tuesday

Start: 2021-11-30 16:00:00+01:00 End: 2021-11-30 17:00:00+01:00

online


Join Zoom Meeting
https://cwi-nl.zoom.us/j/81535249101?pwd=ZDh3Y1kyZmxBUml1Tm00Rkx0NXBwdz09

Meeting ID: 815 3524 9101
Passcode: 967201

Title:      Failure modes of Linkage Learning and how the right neighborhood can solve them
Speaker:    Arthur Guijt
Abstract:   Linkage Learning is at the core of most of GOMEA's scalability improvements, and is a core component of the GOMEA family of approaches. Yet if Linkage Learning fails to work, performance is reduced drastically. In this presentation I'll briefly discuss the current approach, the implicit assumptions that are made, and the failure modes that may occur in practice. Furthermore, I will talk about using neighborhoods - more specifically Local Neighborhoods, how learning linkage locally resolves some of these common failure modes, and how (much) the definition of locality matters.

Title:      Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization
Speaker:    Anton Bouter
Abstract:   It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, e.g., non-separable, multi-modal, and multi-objective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. Such partial evaluations have been applied to various real-world problems, including the optimization of treatment plans for prostate cancer, and deformable image registration.

Members

Associated Members

Publications

Software

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
  • INRIA
  • 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