Artificial Intelligence; Data Mining; High-Throughput Nucleotide Sequencing; Statistics
My research interests include all kind of data analysis. Previously, before I focussed on medical data, I analyzed also data in physics, cryptography, video data and network/telecommunications. The main focus was always biological data, because of my study and because of the more tricky problems. Emphasis is more on the empirical side (= real data). The current points are classification models, pattern search and this or other methods with large amounts of data (sequencing data, also combined with other data).
Techniques, methods & infrastructure
machine learning, statistics (in R, matlab and mathematica), programming (perl, C/C++, java, bash, ...), usage of computer clusters (slurm, SGE).
- 1001 Genomes Consortium, 2016. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell, 166(2), pp.481-491. Available at: https://dx.doi.org/10.1016/j.cell.2016.05.063.
- Watson, J.M. et al., 2016. Germline replications and somatic mutation accumulation are independent of vegetative life span inArabidopsis. Proceedings of the National Academy of Sciences, 113(43), pp.12226-12231. Available at: http://dx.doi.org/10.1073/pnas.1609686113.
- Platzer, A., Nizhynska, V. & Long, Q., 2012. TE-Locate: A Tool to Locate and Group Transposable Element Occurrences Using Paired-End Next-Generation Sequencing Data. Biology, 1(3), pp.395-410. Available at: http://dx.doi.org/10.3390/biology1020395.
- Catchpole, G. et al., 2009. Metabolic profiling reveals key metabolic features of renal cell carcinoma. Journal of Cellular and Molecular Medicine, 15(1), pp.109-118. Available at: http://dx.doi.org/10.1111/j.1582-4934.2009.00939.x.
- Platzer, A. et al., 2007. Characterization of protein-interaction networks in tumors. BMC Bioinformatics, 8(1), p.224. Available at: http://dx.doi.org/10.1186/1471-2105-8-224.