Algorithms for complex data in life sciences

Short Introduction


We aim at discovering mechanisms of biological dysfunction in complex diseases by pursuing a systems view that includes molecular, cellular, tissue, organism and population level aspects.  Hereby we focus on the analysis and functional mining of large and heterogeneous data which includes different kinds of high throughput ‘OMICs’, phenotypic and clinical data.

In particular, we develop and implement bioinformatical algorithms suited for high-dimensional data. A central approach are self-organizing maps (SOMs), which are utilized to reduce the feature and sample space dimensions while preserving the information richness contained, and which we complemented with algorithms for preprocessing, data integration,  feature selection, function mining and visualization. Our R-package for holistic analysis of gene expression data can be downloaded from Bioconductor repository:

Additionally we develop methods to disentangle complex disease entities such as cancer subtypes, and to evaluate the interplay between activated/deactivated cellular functionalities and different disease stages. The latter one allows for the data driven staging of, e.g., cancer samples, and for the recapitulation of cancer progression.

Finally, modeling and simulation experiments supplement clinical data to track cancer progression trajectories. Our algorithms allow to generate a state space network for a given study and to investigate transition paths from healthy cells to diseased states, and from low grade to higher grade stadiums.

Research scope:

  • Systems biology of cancer (lymphoma, glioma, melanoma & hereditary colon cancer):

Analysis of microarray and RNA-seq data to develop a systems view on basal mechanisms of tumor genesis and progression

  • Analysis of body scanner data, evaluation of body types and association to diseases
  • Molecular phenotypes of the population of Leipzig
  • Trajectories in single cell differentiation experiments

Associated projects and partners

  • BMBF funded Projects (MMML-MYC-SYS, HNPCC-SYS, CAP-SYS, Glioma-SYS)
  • LIFE – Leipzig Research Center for Civilization Diseases
  • German Glioma network
  • Hautklinik Leipzig
  • Max Planck Institute for Evolutionary Anthropology
  • IZI – Fraunhofer Institute for Cell Therapy and Immunology


  • Löffler-Wirth, H., Kalcher, M. & Binder, H.: oposSOM: R-package for individualized analysis of genome-wide expression landscapes on Bioconductor. Bioinformatics 2015
  • Weller, M., Weber, R., Willscher, E., Riehmer, V., Hentschel, B., Kreuz, M., Felsberg, J., Beyer, U., Löffler-Wirth, H., Kaulich, K., Steinbach, J., Hartmann, C., Gramatzki, D., Schramm, J., Westphal, M., Schackert, G., Simon, M., Martens, T., Boström, J., Hagel, C., Sabel, M., Krex, D., Tonn, J., Wick, W., Noell, S., Schlegel, U., Radlwimmer, B., Pietsch, T., Löffler, M., von Deimling, A., Binder, H. & Reifenberger, G.: Molecular classification of diffuse cerebral WHO grade II/III gliomas using genome- and transcriptome-wide profiling improves stratification of prognostically distinct patient groups. Acta Neuropathologica 2015
  • Binder, H., Wirth, H., Arakelyan, A., Lembcke, K., Tiys, E., Ivanishenko, V., Nicolay K., Kononikhin, A., Popov, I., Nikolaev, E., Pastushkova, L., & Larina. I.: Time-course human urine proteomics in space-flight simulation experiments – A high resolution and personalized machine learning analysis. BMC Genomics 2014
  • Cakir, V., Binder, H. & Wirth, H.: Profiling of genetic switches using boolean implications in expression data. Journal of Integrative Bioinformatics 2014
  • Charbord, P., Pouget, C., Binder, H., Dumont, F., Stik, G., Levy, P., Allain, F., Marchal, C., Richter, J., Uzan, B., Pflumio, F., Letourneur, F., Wirth, H., Dzierzak, E., Traver, D., Jaffredo, T. & Durand, C.: A Systems Biology Approach for Defining the Molecular Framework of the Hematopoietic Stem Cell Niche. Cell stem cell 2014
  • Reifenberger, G., Weber, R., Riehmer, V., Kaulich, K., Willscher, E., Wirth, H., Gietzelt, J., Hentschel, B., Westphal, M., Simon, M., Schackert, G., Schramm, J., Matschke, J., Sabel, M., Gramatzki, D., Felsberg, J., Hartmann, C., Steinbach, J., Schlegel, U., Wick, W., Radlwimmer, B., Pietsch, T., Tonn, J., von Deimling, A., Binder, H., Weller, M. & Loeffler, M.: Molecular characterisation of long-term survival with glioblastoma using genome- and transcriptome-wide profiling. International Journal of Cancer 2014
  • Binder, H., Hopp, L., Lembcke, K. & Wirth, H.: Personalized disease phenotypes from massive OMICs data. Big Data Analysis in Bioinformatics and Healthcare 2014
  • Wirth, H.: Analyse molekularbiologischer Daten mittels Self-organizing Maps. Lecture Notes in Informatics (“Ausgezeichnete Informatikdissertationen 2012”) 2013
  • Hopp, L.*, Wirth, H.*, Fasold, M. & Binder, H.: Portraying the expression landscapes of cancer subtypes: a glioblastoma multiforme and prostate cancer case study. Systems Biomedicine 2013
  • Hopp, L., Lembcke, K., Binder, H. & Wirth, H.: Portraying the Expression Landscapes of B-Cell Lymphoma – Intuitive Detection of Outlier Samples and of Molecular Subtypes. Biology 2013
  • Wirth, H., von Bergen, M. & Binder, H.: Mining SOM expression portraits: Feature selection and integrating concepts of molecular function. BioData Mining 2012
  • Wirth, H., Löffler, M., von Bergen, M. & Binder, H.: Expression cartography of human tissues using self-organizing maps. BMC Bioinformatics 2011


  • For details see