Computational Population Biology Group

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Computational Population Biology group develops and applies methods for the integrative analysis of large-scale biological, epidemiological and clinical data. Our goals are to improve the understanding of how various omics affect the complex traits and to make use of such insights to improve the diagnosis, prevention and treatment of diseases whenever possible.

The group uses the latest approaches in genomics, medical imaging, computer science, statistics and machine learning to sort through increasingly rich and massive amount of data.

The group has affiliation with two departments: Epidemiology, Radiology and Nuclear Medicine.

The Radiology Department and specifically the Biomedical Imaging Group Rotterdam (BIGR), has years of expertise in machine learning algorithms and medical infrastructure development. Additionally, the group has a large GPU cluster, which makes it perfect place for our AI related projects.

The Epidemiology Department has a long history of successful epidemiological studies, unique facilities of the Rotterdam Study (ERGO study) and the Netherland Institute for Health Sciences (NIHES) educational program, which are important components for knowledge dissemination.  Additionally, the Epidemiology department is well established within the CHARGE consortium.

Expertise

Big data analysis - Omics - Bioinformatics - Epidemiology - Software development - Machine learning - Deep learning - Data Science - Data visualization - 3D Medical Imaging

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Omics analysis

We are developing novel algorithms for omics data analysis to understand better the relationship between them and various complex diseases.

We primarily focused on developing Explainable AI, which are essential for interpretability of the results.

GenNet example of the network architecture

Example of Explainable AI module of GenNet framework

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Additionally, together with NVIDIA, we are working on a Federated Learning framework, which will connect together research centers all over the world. We are primary focused on CHARGE consortium, EADB consortium. Such framework will allow running machine learning and deep learning models without sharing a raw data in a large multi-center settings.

GenNet pipeline for federated learning
NVIDIA clara framework for federated learning

Quantitative traits analysis

To explain the etiology of the complex traits, we not only need the multi-omics approaches, but also the best representation of such traits, i.e. endophenotypes. Therefore, we are interested in developing methods to derive such endophenotypes.

We are working on several domains:

Data visualization and accessibility

Knowledge dissemination and data accessibility is essential part of nowadays research. The group is dealing daily with enormous amount of complex data, therefore it is important to find a way to reach out another researchers and clinicians to provide them the opportunity to use and explore the results of our work.

We focus on developing open source software and online tools for data visualization, research and educational propose.

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Collaborations

The group actively involved in various collaboration projects within Erasmus MC Medical Center:

  • Departments of Epidemiology
  • Radiology and Nuclear Medicine
  • Neurosurgery
  • Plastic Surgery
  • Internal Medicine
  • Psychiatry
  • Neuroscience
  • Craniomaxillofacial surgery
  • Genetic Identification

Also nationally and internationally:

The group members contribute as AI experts in the EU COST actions “GEnomics of MusculoSkeletal traits” and “ML4Microbiome”.

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