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Auflistung nach Autor:in "Zimmer, Ralf"

1 - 9 von 9
  • Zeitschriftenartikel
    Algorithmische Systembiologie mit Petrinetzen – Von qualitativen zu quantitativen Systemmodellen
    (Informatik-Spektrum: Vol. 32, No. 4, 2009) Birzele, Fabian; Csaba, Gergely; Erhard, Florian; Friedel, Caroline; Küffner, Robert; Petri, Tobias; Windhager, Lukas; Zimmer, Ralf
    Die algorithmische Systembiologie ist ein aktuelles Teilgebiet der Bioinformatik. Petrinetze [28] werden seit Jahrzehnten für die Modellierung von Systemen und seit ca. 15 Jahren auch in der netzwerk-orientierten Bioinformatik [17, 27] verwendet. In der algorithmischen Systembiologie werden Petrinetze einerseits zur Repräsentation von biologischem Wissen in Form von qualitativen Netzwerkmodellen und andererseits für die Analyse ihrer dynamischen Eigenschaften und ihre quantitative Simulation eingesetzt. Die Auswahl relevanter Teilnetze aus großen qualitativen Netzen und ihre Umsetzung in quantitative Modelle erfordern neue methodische Ansätze.
  • Zeitschriftenartikel
    Bioinformatics advances biology and medicine by turning big data troves into knowledge
    (Informatik Spektrum: Vol. 40, No. 2, 2017) Gagneur, Julien; Friedel, Caroline; Heun, Volker; Zimmer, Ralf; Rost, Burkhard
    Informatics and life sciences (molecular biology and medicine) are undoubtedly the most rapidly growing and most dynamic endeavors of modern society. Computational biology or bioinformatics describes the rising field that integrates those endeavors. Over the last 50 years, the field has shifted focus from the study of individual genes and proteins (1967–1994), to that of entire organisms (19952015), and more recently to studying the diversity of populations. The increasing amount of big data created by the life sciences is challenging already by its volume alone. Even more challenging is the high intrinsic complexity of the data. In addition, the data are changing at a breathtaking speed; most data generated in 2016 probes conditions that had not been anticipated 15 years ago. Precision medicine and personalized health are just two descriptors of how modern biology will become relevant for improving our health. All new drugs have at some point have bioinformatics tools in their development. Similarly, there would not be any digital medicine without the bioinformatics expertise or any advances without mastering machine learning tools turning raw data into valuable insights and decisions.
  • Konferenzbeitrag
    Characterization of protein interactions
    (German Conference on Bioinformatics, 2006) Küffner, Robert; Duchrow, Timo; Fundel, Kartin; Zimmer, Ralf
    Available information on molecular interactions between proteins is currently incomplete with regard to detail and comprehensiveness. Although a number of repositories are already devoted to capture interaction data, only a small subset of the currently known interactions can be obtained that way. Besides further experiments, knowledge on interactions can only be complemented by applying text extraction methods to the literature. Currently, information to further characterize individual interactions can not be provided by interaction extraction approaches and is virtually nonexistent in repositories. We present an approach to not only confirm extracted interactions but also to characterize interactions with regard to four attributes such as activation vs. inhibition and protein-protein vs. protein-gene interactions. Here, training corpora with positional annotation of interacting proteins are required. As suitable corpora are rare, we propose an extensible curation protocol to conveniently characterize interactions by manual annotation of sentences so that machine learning approaches can be applied subsequently. We derived a training set by manually reading and annotating 269 sentences for 1090 candidate interactions; 439 of these are valid interactions, predicted via support vector machines at a precision of 83% and a recall of 87%. The prediction of interaction attributes from individual sentences on average yielded a precision of about 85% and a recall of 73%.
  • Konferenzbeitrag
    Combining secondary structure element alignment and profile-profile alignment for fold recognition
    (German Conference on Bioinformatics 2004, GCB 2004, 2004) Gewehr, Jan E.; Öhsen, Niklas von; Zimmer, Ralf
    One of the most intensely studied problems of bioinformatics is the prediction of a protein structure from an amino acid sequence. In fold recognition, one reduces this problem to assigning a protein of unknown structure to one of the known fold classes as defined in the SCOP or CATH classifications. Here, we combine two alignment methods, secondary structure element alignment and log average profile- profile alignment that have been proven to perform well on this task. Our results show that the combination yields remarkably better fold recognition accuracy on well- known benchmark sets obtained from the literature. Especially on a difficult set built by McGuffin and Jones this new approach significantly outperforms other recently proposed fold recognition methods.
  • Konferenzbeitrag
    Data processing effects on the interpretation of microarray gene expression experiments
    (German Conference on Bioinformatics 2005 (GCB 2005), 2005) Fundel, Katrin; Küffner, Robert; Aigner, Thomas; Zimmer, Ralf
    Motivation: Microarray gene expression data is collected at an increasing pace and numerous methods and tools exist for analyzing this kind of data. The aim of this study is to evaluate the effect of the basic statistical processing steps of microarray data on the final outcome for gene expression analysis; these effects are most problematic for one-channel cDNA measurements, but also affect other types of microarrays, especially when dealing with grouped samples. It is crucial to determine an appropriate combination of individual processing steps for a given dataset in order to improve the validity and reliability of expression data analysis. Results: We analyzed a large gene expression data set obtained from a one-channel cDNA microarray experiment conducted on 83 human samples that have been classified into four Osteoarthritis related groups. We compared different normalization methods regarding the effect on the identification of differentially expressed genes. Furthermore, we compared different methods for combining spot p-values into gene p-values, and propose Stouffer's method for this purpose. We developed several quality and robustness measures which allow to estimate the amount of errors made in the statistical data preparation. Conclusion: The apparently straight forward steps of gene expression data analysis, i.e. normalization and identification of differentially expressed genes, can be accomplished by numerous different methods. We analyzed multiple combinations of a number of methods to demonstrate the possible effects and therefore the importance of the single decisions taken during data processing. An overview of these effects is essential for the biological interpretation of gene expression measurements. We give guidelines and tools for evaluating methods for normalization, spot combination and detection of differentially regulated genes.
  • Konferenzbeitrag
    Identifying the topology of protein complexes from affinity purification assays
    (German Conference on Bioinformatics, 2008) Friedel, Caroline C.; Zimmer, Ralf
    Recent advances in high-throughput technologies have made it possible to investigate not only individual protein interactions but the association of these proteins in complexes. So far the focus has been on the prediction of complexes as sets of proteins from the experimental results while the modular substructure and the physical interactions within protein complexes have been mostly ignored. In this article, we present an approach for identifying the direct physical interactions and the subcomponent structure of protein complexes predicted from affinity purification assays. Our algorithm calculates the union of all maximum spanning trees from scoring networks for each protein complex to extract relevant interactions. In a subsequent step this network is extended to interactions which are not accounted for by alternative indirect paths. We show that the interactions identified with this approach are more accurate in predicting experimentally derived physical interactions than baseline approaches and resolve more satisfactorily the subcomponent structure of the complexes. The usefulness of our approach is illustrated on the RNA polymerases for which the modular substructure can be successfully reconstructed with our method.
  • Konferenzbeitrag
    Intuitive Modeling of Dynamic Systems with Petri Nets and Fuzzy Logic
    (German Conference on Bioinformatics, 2008) Windhager, Lukas; Zimmer, Ralf
    Current approaches in modeling dynamic biological systems often lack comprehensibility, especially for users without mathematical background. We pro- pose a new approach to overcome such limitations by combining the graphical representation provided by the use of Petri nets with the modeling of dynamics by powerful yet intuitive fuzzy logic based systems. The mathematical functions and formulations typically used to describe or quantify dynamic changes of systems are replaced by if-then rules, which are both easy to read and formulate. Precise values of kinetic constants or concentrations are substituted by more natural fuzzy representations of entities. We will show that our new approach allows a semi-quantitative modeling of biological systems like signal transduction pathways or metabolic processes while not being limited to those cases.
  • Konferenzbeitrag
    Probabilistic methods for predicting protein functions in protein-protein interaction networks
    (German Conference on Bioinformatics 2004, GCB 2004, 2004) Best, Christoph; Zimmer, Ralf; Apostolakis, Joannis
    We discuss probabilistic methods for predicting protein functions from protein-protein interaction networks. Previous work based on Markov Randon Fields is extended and compared to a general machine-learning theoretic approach. Using actual protein interaction networks for yeast from the MIPS database and GO-SLIM function assignments, we compare the predictions of the different probabilistic methods and of a standard support vector machine. It turns out that, with the currently available networks, the simple methods based on counting frequencies perform as well as the more sophisticated approaches.
  • Konferenzbeitrag
    Veranstaltung zum Tagungsmotto: Bioinformatik und Biotechnologie
    (INFORMATIK 2008. Beherrschbare Systeme - dank Informatik. Band 2, 2008) Heun, Volker; Zimmer, Ralf