Proteomics is the large-scale study of the (human) proteome. Modern proteomics utilizes modern measurement procedures from analytical chemistry - most notably mass spectrometry – which allows proteomic expression to be evaluated on a large number of proteins simultaneously for patient samples. Mass spectrometry measurements yield a special data type as the recorded spectral responses are histogram functions of observed protein intensities across a mass range. Proper statistical analysis must take this feature of the data into account.
The department is currently involved in several projects in clinical proteomics, in collaboration with the Department of Surgery and the Biomolecular Mass Spectrometry unit of the Department of Parasitology.
Our department carries out research on the following topics, which are closely related to our involvement in consultation for proteomic studies.
- Analysis of case-control data for construction of new diagnostic procedures or for identification of novel proteomic biomarkers for enhanced detection of disease.
- Analysis of prognostic studies using spectra for prediction of outcome and to handle censoring in proteomic prospective study designs.
- Development and application of validation and evaluation methods in the calibration of prognostic or diagnostic predictors.
- Construction of conditional independence graphs (graphical models) to aid the identification of new proteins from the expression of known proteins.
- Construction and application of peak-selection methods in the calibration of proteomic predictors using mass spectra.
- Methods to allow combination of proteomic information obtained from paired (multiple) spectra per patient.
The department has organized and hosted the International Competition on Mass Spectrometry Proteomic Diagnosis (2008).
- Mertens, B.J.A. Statistical Applications in Genetics and Molecular Biology. Volume 7, Issue 2, Pages –, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1348, January 2008
- Mertens, B.J.A., van der Burgt, Y.E.M., Velstra, B., Mesker, W.E., Tollenaar, R.A.E.M., Deelder, A.M. (2011) On the use of double cross-validation for the combination of proteomic mass spectral data for enhanced diagnosis and prediction. Statistics and Probability Letters, Volume 81, Issue 7, 759-766.
- Mertens, B.J.A., Tollenaar, R.A.E.M., Deelder, A.M. (2006) Mass Spectrometry Proteomic Diagnosis:Enacting the Double Cross-Validatory Paradigm. Journal of Computational Biology, Volume 13, Number 9, 1591-1605.
- Mertens, B.J.A. (2007) Logistic Regression Modelling of Proteomic Mass Spectra in a Case-Control Study on Diagnosis for Colon Cancer. Bayesian Statistics 8, 637-642.