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Clinical Bioinformatics @ ErasmusMC

Contact Details

Erasmus University Medical Center

Andrew Stubbs


Hotel Description

The Clinical Bioinformatics team develops innovative methods for supporting translational research and robust applications for clinical diagnostics.  Our research is focused on developing novel methods for fusion gene detection from RNASeq and DNAseq, implementing and validating cross-platform statistical methods for patient stratification and biomarker discovery, novel Galaxy services and FAIR data applications. Our core skills are in bioinformatics, biostatistics, database development and biomarker discovery.  Our Diagnostic group develops or implements applications to support our pathology services, including molecular and digital pathology, biobanking and new methods for patient stratification.



  • Biomedical & health
  • Clinical Diagnostics
  • Biomarker Discovery
  • FAIR Data
  • Galaxy
  • Cardiology
  • Immunology
  • Microbiology
  • Oncology

Expertise and Track Record

43500984072 MolDia2All: An open source database and reporting platform for Molecular Pathology in the Netherlands with W.W.J. de Leng (UMC Utrecht)

Galaxy Application Development and IUC Galaxy Training.

myFAIR Analysis – self service data management and analysis complying to FAIR data priciples

  • Hands on Workshop on data management tools for translational research. How to make your data FAIR with  myFAIR Analysis


A diagnostic device is in development that will identify – within 1 hour – a wide range of viruses and bacteria responsible for respiratory and oral infections, including major viral and bacterial pathogens associated with global morbidity and mortality.

  1. Chair/bedside diagnosis of oral and respiratory tract infections, and identification of antibiotic resistances for personalised monitoring and treatment. Mitsakakis K, et al. Stud Health Technol Inform. 2016;224:61-6

ENS@T-CANCER (FP7/2007-2013)

The ultimate aim of the Consortium is to develop research in the field of adrenal cancers to improve diagnosis and treatment abilities.  We have developed an automated hotspot detection platform (ASH) and validated ASH in a reproducability study

  1. An International Ki67 Reproducibility Study in Adrenal Cortical Carcinoma. Papathomas TG, et al .Am J Surg Pathol. 2016 Apr;40(4):569-76.
  2. Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer. Lu H, et al. Diagn Pathol. 2014 Nov 25;9:216.

Biomarkers and Patient Stratifciation

  1. Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Gravendeel LA, et al. Cancer Res (2009)

CTMM Triumph

  • Heart failure is a progressive disease with major impact on patients and society. The TRIUMPH project identified biomarkers (both blood-based and tissue-based) that are used to assess the level and nature of left ventricle overload and predict patient outcome.  http://www.ctmm.nl/nl/downloadsnl-pdf/output-reports/triumph-output-report
  • Clinical Bioinformatics @ Erasmus was the WP lead for bioinformatics and developed a  cross platform cross species biomarker discovery methodology.
  1. NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data. McClellan EA, et al. Bioinformatics. 2013 Nov 15;29(22):2946-7.

CTMM Circulating cells

  • Patients suffering from atherosclerotic plaques typically exhibit few or no symptoms until an unstable plaque disrupts and leads to the formation of a clot that obstructs the blood flow. The results are usually catastrophic (e.g. stroke or heart attack).  We have idenified and are clinically validating biomarkers to discriminate patients that exhibit these so-called ‘unstable vulnerable plaques.  http://www.ctmm.nl/en/downloads/projects-downloads/circulating-cells-output-report.
  • Clinical Bioinformatics @ Erasmus was the WP lead for bioinformatics.  We developed new statistical pathway and analysis and developed predictive models for clinical outcome from our novel biomarkers.
  1. Treatment variation in stent choice in patients with stable or unstable coronary artery disease.Burgers LT, et al. Neth Heart J. 2016 Feb;24(2):

Hotel Characteristics