Dr. Djork-Arné Clevert

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Persönliche Daten/Ausbildung

  • Born June 18, 1972
  • Married, two children
  • 1992-2006: Technische Universität Berlin, BA in energy and processing technology, BA in computer science
  • graduation: MSc in computer science
  • 2008-2011: Charité University Medicine: PhD


  • Deep learning
  • Convolutional network
  • Next generation sequencing / microarrays
  • Analysis of SNP-data and CNV-data
  • Medical approaches of bioinformatics
  • Alternative splicing
  • Machine Learning

Beruflicher Werdegang

  • 2013: Marie Curie Experienced Researcher Fellowship
  • 2013-2014: Senior Data Scientist, Zalando AG, Berlin
  • 2013-2013: Data Scientist, AG, Berlin
  • 2007-2013: Research Associate, Johannes Kepler University, Linz
  • 2006-2007: Bioinformatiker, Signature-Diagnostics AG, Germany, Potsdam
  • 1998-1999: Student Research Assistant, Technische Universität Berlin
  • 1998-2008: Freelancer, QuaSi-Niere, German Register for Quality Control in Renal Replacement Therapy


  • Djork-Arne Clevert, Thomas Unterthiner, Sepp Hochreiter: Rectified Factor Networks for BiclusteringInternational Conference on Learning Representations (ICLR 2016 Workshop Track)
  • Djork-Arne Clevert, Thomas Unterthiner, Sepp Hochreiter: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)International Conference on Learning Representations (ICLR 2016 Conference Track)link
  • Martin Heusel, Djork-Arne Clevert, Günter Klambauer, Andreas Mayr, Karin Schwarzbauer, Thomas Unterthiner, Sepp Hochreiter: ELU-Networks: Fast and Accurate CNN Learning on ImageNetImageNet and MS COCO Visual Recognition Challenges Joint Workshop, in conjunction with International Conference on Computer Vision (ICCV), 2015
  • Djork-Arne Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter: Rectified Factor NetworksAdvances in Neural Information Processing Systems 28 (NIPS 2015)link
  • SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium, Nature Biotechnology, 32:903-914, 2014
  • Munro S. A., Lund S. P.,Pine S. P., Binder H., Clevert D. A., et al. (2014). Assessing Technical Performance in Differential Gene Expression Experiments with External Spike-in RNA Control Ratio Mixtures. Nature Communications 5125, 25 September 2014 doi:10.1038/ncomms6125
  • Clevert, D. A., Mayr, A., Mitterecker, A., Klambauer, G., Valsesia, A., Forner, K., Tuefferd, M., Talloen, W., Wojcik, J., Göhlmann, H., & Hochreiter, S. (2013). Increasing the discovery power of-omics studies. Systems Biomedicine, 1(2), 0-1. Link
  • Klambauer, G., Schwarzbauer, K., Mayr, A., Clevert, D. A., Mitterecker, A., Bodenhofer, U., & Hochreiter, S. (2012). cn. MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Research, 40(9), e69-e69. doi:10.1093/nar/gks003
  • Clevert, D. A., Mitterecker, A., Mayr, A., Klambauer, G., Tuefferd, M., De Bondt, A., Talloen, W., Göhlmann, H., & Hochreiter, S. (2011). cn. FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate. Nucleic Acids Research, 39(12), e79-e79. doi:10.1093/nar/gkr197
  • Hochreiter S., Bodenhofer W., Heusel M., A. Mayr A., Mitterecker A., Kasim A., Khamiakova T., Van Sanden S., Lin D., Talloen W., Bijnens L., Göhlmann H., Shkedy Z., & Clevert D.-A., FABIA: Factor Analysis for Bicluster Acquisition, Bioinformatics, 2010, 26(12):1520-7
  • Kasim A., Miss D. L., Van Sanden S., Clevert D.-A., Bijnens L., Göhlmann H., Amaratunga D., Hochreiter S., Shkedy Z., and Talloen W., Informative or Non-informative Calls for Gene Expression: A Latent Variable Approach, Statistical Applications in Genetics and Molecular Biology, 2010, Vol. 9: Iss. 1, 4
  • Tuefferd M., De Bondt A., Van Den Wyngaert I., Talloen W., Verbeke T., Carvalho B., Clevert D.-A., Alifano M.,Raghavan N., Amaratunga D., Göhlmann H., Broët P., & Camilleri-Broët S., Genome-wide copy number alterations detection in fresh frozen and matched FFPE samples using SNP 6.0 arrays, Genes, Chromosomes & Cancer, 2008, 47(11):957-64
  • Talloen W., Clevert D.-A., Hochreiter S., Amaratunga D., Göhlmann H.W.H., I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data, Bioinformatics, 2007, 23:2897-2902
  • Hochreiter S., Clevert D.-A., and Obermayer K., A New Summarization Method for Affymetrix Probe Level Data, Bioinformatics, 2006, 15;22(8):943-9
  • Clevert et al. Color Doppler, power Doppler and B-flow ultrasound in the assessment of ICA stenosis: Comparison with 64-MD-CT angiography, Eur. Radiol., 2006 Nov 22
  • Clevert et al., Imaging of aortic abnormalities with contrast-enhanced ultrasound. A pictorial comparison with CT, Eur. Radiol. 2007
  • Clevert et al., High-grade stenoses of the internal carotid artery: Comparison of high-resolution contrast enhanced 3D MRA, duplex sonography and power Doppler imaging, Eur J Radiol. 2006
  • Clevert et al., Contrast-enhanced ultrasound versus MS-CT in blunt abdominal trauma, Clin Hemorheol Microcirc. 2008; 39(1-4):155-69
  • Clevert et al., Color duplex ultrasound and contrast-enhanced ultrasound in comparison to MS-CT in the detection of endoleak following endovascular aneurysm repair, Clin Hemorheol Microcirc. 2008, 39(1-4):121-32
  • Clevert et al.,Contrast-enhanced ultrasound versus conventional ultrasound and MS-CT in the diagnosis of abdominal aortic dissection, Clin Hemorheol Microcirc. 2009, 43(1-2):129-139
  • Clevert et al.,Modern ultrasound diagnostics of deep vein thrombosis in lung embolism of unknown origin, Radiologe, 2007, 47(8):673-684
  • Clevert et al., Chip card assisted safety of communication in German public health, Stud Health Technol Inform: 77(2) 1096-7, 2000