Logo der Universitätsmedizin Mainz
Institut für Medizinische Biometrie,
Epidemiologie und Informatik


Prof. Dr. Harald Binder

Harald Binder

Gebäude 902
2. Etage, Raum 204

Tel 06131 17-3938
Fax 06131 17-2968
Mail  binderh@uni-mainz.de




Wissenschaftliche Schwerpunkte

  • Techniken der statistischen Bioinformatik für hochdimensionale molekulare Daten (z.B. RNA-Seq-Workflows)
  • Integrative Datenanalyse mehrerer molekularer Ebenen oder Gruppen von Individuen
  • Dynamische Modellierung
  • Regressionsmodelle zur Risikovorhersage

Ausgewählte statistische und bioinformatische Publikationen

  • Itzel, T., Scholz, P., Maass, T., Krupp, M., Marquardt, J.U., Strand, S., Becker, D., Staib. F., Binder, H., Roessler, S., Wang, X.W., Thorgeirsson, S., Müller, M., Galle, P.R., and Teufel, A. (2015). Translating bioinformatics in oncology: Guilt-by-profiling analysis and identification of KIF18B and CDCA3 as novel driver genes in carcinogenesis. Bioinformatics. 31(2): 216-224.

  • Hieke, S., Binder, H., Nieters, A., and Schumacher, M. (2014). minPtest: A resampling based gene region-level testing procedure for genetic case-control studies. Computation Stat., 29(1-2), 51-63.

  • König, J., Krahn, U., and Binder, H. (2014). Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Stat Med, 32(30), 5414-5429.
  • Krahn, U., Binder, H., and König, J. (2014). Visualizing inconsistency in network meta-analysis by independent path decomposition. BMC Med Res Methodol, 14:131.

  • Mayr, A., Binder, H., Gefeller, O., and Schmid, M. (2014). The evolution of boosting algorithms: from machine learning to statistical modelling. Method Inform Med, 53(6), 419-427.

  • Mayr, A., Binder, H., Gefeller, O., and Schmid, M. (2014). Extending statistical boosting: an overview of recent methodological developments. Method Inform Med, 53(6), 428-435.

  • Sariyar, M., Hoffman, I., and Binder, H. (2014). Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data. BMC Bioinformatics, 15, 58.

  • Sariyar, M., Schumacher, M., and Binder, H. (2014). A boosting approach for adapting the sparsity of risk prediction signatures based on different molecular levels. Stat Appl Genet Mol, 13(3), 343-357.

  • Schmidtmann, I., Elsäßer, A., Weinmann, A., and Binder, H. (2014). Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry. Stat Med, 33(30), 5358-5370.

  • Zwiener, I., Frisch, B., and Binder, H. (2014). Transforming RNA-Seq data to improve the performance of prognostic gene signatures. PLOS ONE, 9(1), e85150.

  • Binder, H., Benner, A., Bullinger, L., and Schumacher, M. (2013). Tailoring sparse multivariable regression techniques for prognostic single-nucleotide polymorphism signatures. Stat Med, 32(10), 1778-1791.

  • Binder, H., Sauerbrei, W., and Royston, P. (2013). Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: A simulation study with continuous response. Stat Med, 32(13), 2262-2277.

  • Knaus, J., Hieke, S., Binder, H., and Schwarzer, G. (2013). Costs of cloud computing for a biometry department. A case study. Method Inform Med, 52(1), 72-79.

  • Krahn, U., Binder, H., and König, J. (2013). A graphical tool for locating inconsistency in network meta-analyses. BMC Med Res Methodol, 13(1), 35.

  • Binder, H., Müller, T., Schwender, H., Golka, K., Steffens, M., Hengstler, J. G., Ickstadt, K., and Schumacher, M. (2012). Cluster-localized sparse logistic regression for SNP data. Stat Appl Genet Mol, 11, 4.

  • Binder, H., Porzelius, C., and Schumacher, M. (2011). An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models. Biometrical J, 53, 170-189.
  • Gade, S., Porzelius, C., Fälth, M., Brase, J. C., Wuttig, D., Kuner, R., Binder, H., Sültmann, H., and Beißbarth, T. (2011). Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer. BMC Bioinformatics, 12(1), 488.

  • Porzelius, C., Johannes, M., Binder, H., and Beißbarth, T. (2011). Leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients. Biometrical J, 53(2), 190-201.

  • Porzelius, C., Schumacher, M., and Binder, H. (2011). The benefit of data-based model complexity selection via prediction error curves in time-to-event data. Computation Stat, 26(2), 293-302.

  • Rücker, G., Reiser, V., Motschall, E., Binder, H., Meerpohl, J. J., Antes, G., and Schumacher, M. (2011). Boosting qualifies capture-recapture methods for estimating the comprehensiveness of literature searches for systematic reviews. J Clin Epidemiol, 64(12), 1364-72.

  • Rücker, G., Schwarzer, G., Carpenter, J. R., Binder, H., and Schumacher, M. (2011). Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis. Biostatistics, 12(1), 122-42.

  • Sauerbrei, W., Boulesteix, A.-L., and Binder, H. (2011). Stability investigations of multivariable regression models derived from low- and high-dimensional data. J Biopharm Stat, 21(6), 1206-1231.

  • Schoop, R., Beyersmann, J., Schumacher, M., and Binder, H. (2011). Quantifying the predictive accuracy of time-to-event models in the presence of competing risks. Biometrical J, 53(1), 88-112.

  • Binder, H. und Sauerbrei, W. (2010). Adding local components to global functions for continuous covariates in multivariable regression modeling. Stat Med, 29(7-8), 808-817.

  • Porzelius, C., Schumacher, M., and Binder, H. (2010). A general, prediction error-based criterion for selecting model complexity for high-dimensional survival models. Stat Med, 29(7-8), 830-838.

  • Porzelius, C., Schumacher, M., and Binder, H. (2010b). Sparse regression techniques in low-dimensional survival settings. Stat Comput, 20(2), 151-163.
IF: 1.977
  • Binder, H., Allignol, A., Schumacher, M., and Beyersmann, J. (2009). Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics, 25, 890-896.

  • Binder, H. and Schumacher, M. (2009). Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics, 10, 18.

  • Knaus, J., Porzelius, C., Binder, H., and Schwarzer, G. (2009). Easier parallel computing in R with snowfall and sfCluster. The R Journal, 1(1), 54-59.
  • Porzelius, C., Binder, H., and Schumacher, M. (2009). Parallelized prediction error estimation for evaluation of high-dimensional models. The R Journal, 25, 827-829.

  • Binder, H. and Sauerbrei, W. (2008). Increasing the usefulness of additive spline models by knot removal. Comput Stat Data An, 52: 5305-5318.

  • Binder, H. and Schumacher, M. (2008). Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples. Stat Appl Genet Mol, 7, 12.

  • Binder, H. and Schumacher, M. (2008). Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics, 9, 14.

  • Sauerbrei, W., Royston, P., and Binder, H. (2007). Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med, 26, 5512-5528.

  • Schumacher, M., Binder, H., and Gerds, T. A. (2007). Assessment of survival prediction models based on microarray data. Bioinformatics, 23, 1768-1774.

  • Tutz, G. and Binder, H. (2007). Boosting ridge regression. Comput Stat Data An, 51, 6044-6059.

Ausgewählte medizinische Publikationen

  • Schupp, J.C., Binder, H., Jäger, B., Cillis, G., Zissel, G., Müller-Quernheim, J., and Prasse A. (2015). Macrophage activation in acute exacerbation of idiopathic pulmonary fibrosis. PLOS ONE,10(1), e0116775.

  • Zeller. T., Ojeda, F., Brunner, F.J., Peitsmeyer, P., Münzel, T., Binder, H., Pfeiffer, N., Michal, M., Wild, P.S., Blankenberg, S., and Lackner, K.J. (2015). High-sensitivity cardiac troponin I in the general population - defining reference populations for the determination of the 99th percentile in the Gutenberg Health Study. Clin Chem Lab Med. doi: 10.1515/cclm-2014-0619

  • Kodahl, A.R., Lyng, M.B., Binder, H., Cold, S., Gravgaard, K., Knopp, A.S., and Ditzel, H.j. (2014). Novel circulating microRNA signature as a potential non-invasive multi-marker test in ER-positive early-stage breast cancer: A case control study. Mol Oncol. 8(5), 874-883.

  • Kodahl, A.R., Zeuthen, P., Binder, H., Knoop, A.S., Ditzel, H.J. (2014). Alterations in circulating miRNA levels following early-stage estrogen receptor-positive breast cancer resection in post-menopausal women. PLOS ONE, 9(7), e101950.

  • Ponto, K.A., Schuppan, D., Zwiener, I., Binder, H., Mirshahi, A., Diana, T., Pitz, S., Pfeiffer, N., and Kahaly, G.J. (2014). Thyroid-associated orbitopathy is linked to gastrointestinal autoimmunity. Clin Exp Immunol, 178(1), 57-64.

  • Stratz, C., Nührenberg, T., Fiebich, B. L., Amann, M., Kumar, A., Binder, H., Hoffmann, I., Valina, C., Hochholzer, W., Trenk, D., and Neumann, F.-J. (2014). Controlled type II diabetes mellitus has no major influence on platelet micro-RNA expression. Thromb Haemostasis, 111(5), 902-911.

  • Nührenberg, T. G., Langwieser, N., Binder, H., Kurz, T., Stratz, C., Kienzle, R. P., Trenk, D., Zollnhöfer-Momm, D., and Neumann, F. J. (2013). Transcriptome analysis in patients with progressive coronary artery disease: Identification of differential gene expression in peripheral blood. J Cardiovasc Transl, 6(1), 81-93.

  • Becker, D., Sfakianakis, I., Krupp, M., Staib, F., Gerhold-Ay, A., Victor, A., Binder, H., Blettner, M., Maass, T., Thorgeirsson, S., Galle, P. R., and Teufel, A. (2012). Genetic signatures shared in embryonic liver development and liver cancer define prognostically relevant subgroups in HCC. Mol Cancer, 11(1), 55.

  • Stratz, C., Nührenberg, T. G., Binder, H., Valina, C. M., Trenk, D., Hochholzer, W., Neumann, F. J., and Fiebich, B. L. (2012). Micro-array profiling exhibits remarkable intra-individual stability of human platelet micro-RNA. Thromb Haemostasis, 107(4), 634-41.

  • Sreseli, R. T., Binder, H., Kuhn, M., Digel, W., Veelken, H., Sienel, W., Passlick, B., Schumacher, M., Martens, U. M., and Zimmermann, S. (2010). Identification of a 17-protein signature in the serum of lung cancer patients. Oncol Rep, 24(1), 263-270.

  • Landgrebe, M., Binder, H., Koller, M., Eberl, Y., Kleinjung, T., Eichhammer, P., Graf, E., Hajak, G., and Langguth, B. (2008). Design of a placebo-controlled, randomized study of the efficacy of repetitive transcranial magnetic stimulation for the treatment of chronic tinntius. BMC Psychiatry, 8, 23.

  • Langguth, B., Kleinjung, T., Marienhagen, J., Binder, H., Sand, P. G., Hajak, G. and Eichhammer, P. (2007). Transcranial magnetic stimulation for the treatment of tinnitus: effects on cortical excitability. BMC Neurosci, 8, 45.