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  • Frohlich, H  (77)
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  • 1
    Language: English
    In: PLoS ONE, 2011, Vol.6(10), p.e25364
    Description: Diagnostic and prognostic biomarkers for cancer based on gene expression profiles are viewed as a major step towards a better personalized medicine. Many studies using various computational approaches have been published in this direction during the last decade. However, when comparing different gene signatures for related clinical questions often only a small overlap is observed. This can have various reasons, such as technical differences of platforms, differences in biological samples or their treatment in lab, or statistical reasons because of the high dimensionality of the data combined with small sample size, leading to unstable selection of genes. In conclusion retrieved gene signatures are often hard to interpret from a biological point of view. We here demonstrate that it is possible to construct a consensus signature from a set of seemingly different gene signatures by mapping them on a protein interaction network. Common upstream proteins of close gene products, which we identified via our developed algorithm, show a very clear and significant functional interpretation in terms of overrepresented KEGG pathways, disease associated genes and known drug targets. Moreover, we show that such a consensus signature can serve as prior knowledge for predictive biomarker discovery in breast cancer. Evaluation on different datasets shows that signatures derived from the consensus signature reveal a much higher stability than signatures learned from all probesets on a microarray, while at the same time being at least as predictive. Furthermore, they are clearly interpretable in terms of enriched pathways, disease associated genes and known drug targets. In summary we thus believe that network based consensus signatures are not only a way to relate seemingly different gene signatures to each other in a functional manner, but also to establish prior knowledge for highly stable and interpretable predictive biomarkers.
    Keywords: Research Article ; Biology ; Computer Science ; Medicine ; Genetics And Genomics ; Molecular Biology ; Computational Biology ; Oncology ; Computer Science ; Pathology ; Biochemistry
    E-ISSN: 1932-6203
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  • 2
    In: PLoS ONE, 2013, Vol.8(6)
    Description: Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.
    Keywords: Research Article ; Biology ; Computer Science
    E-ISSN: 1932-6203
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  • 3
    In: PLoS ONE, 2013, Vol.8(9)
    Description: Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typical low reproducibility of signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. We here propose a technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier. This is done by smoothing t-statistics of individual genes or miRNAs over the structure of a combined protein-protein interaction (PPI) and miRNA-target gene network. A permutation test is conducted to select features in a highly consistent manner, and subsequently a Support Vector Machine (SVM) classifier is trained. Compared to several other competing methods our algorithm reveals an overall better prediction performance for early versus late disease relapse and a higher signature stability. Moreover, obtained gene lists can be clearly associated to biological knowledge, such as known disease genes and KEGG pathways. We demonstrate that our data integration strategy can improve classification performance compared to using a single data source only. Our method, called stSVM, is available in R-package netClass on CRAN ( http://cran.r-project.org ).
    Keywords: Research Article ; Biology ; Computer Science ; Engineering ; Mathematics ; Medicine
    E-ISSN: 1932-6203
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  • 4
    In: Bioinformatics, 2015, Vol. 31(20), pp.3290-3298
    Description: In the last years there has been an increasing effort to computationally model and predict the influence of regulators (transcription factors, miRNAs) on gene expression. Here we introduce biRte as a computationally attractive approach combining Bayesian inference of regulator activities with network reverse engineering. biRte integrates target gene predictions with different omics data entities (e.g. miRNA and mRNA data) into a joint probabilistic framework. The utility of our method is tested in extensive simulation studies and demonstrated with applications from prostate cancer and Escherichia coli growth control. The resulting regulatory networks generally show a good agreement with the biological literature. Availability and implementation: biRte is available on Bioconductor ( http://bioconductor.org ). Contact: frohlich@bit.uni-bonn.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Keywords: Biology;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
    E-ISSN: 13674811
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  • 5
    In: Biometrical Journal, March 2014, Vol.56(2), pp.287-306
    Description: Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step toward a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Most of these methods focus on classification problems, that is learn a model from data that discriminates patients into distinct clinical groups. Far less has been published on approaches that predict a patient's event risk. In this paper, we investigate eight methods that integrate network information into multivariable Cox proportional hazard models for risk prediction in breast cancer. We compare the prediction performance of our tested algorithms via cross‐validation as well as across different datasets. In addition, we highlight the stability and interpretability of obtained gene signatures. In conclusion, we find GeneRank‐based filtering to be a simple, computationally cheap and highly predictive technique to integrate network information into event time prediction models. Signatures derived via this method are highly reproducible.
    Keywords: Biomarker ; Cox Regression ; Gene Signature ; Network Information
    ISSN: 0323-3847
    E-ISSN: 1521-4036
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  • 6
    Language: English
    In: PLoS ONE, 01 January 2015, Vol.10(11), p.e0142646
    Description: Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy...
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 7
    In: PLoS ONE, 2017, Vol.12(2)
    Description: Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Computer And Information Sciences ; Biology And Life Sciences ; Physical Sciences ; Research And Analysis Methods ; Biology And Life Sciences ; Physical Sciences ; Computer And Information Sciences ; Physical Sciences ; Biology And Life Sciences ; Biology And Life Sciences
    E-ISSN: 1932-6203
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  • 8
    In: Movement Disorders, November 2015, Vol.30(13), pp.1794-1801
    Description: To purchase or authenticate to the full-text of this article, please visit this link: http://onlinelibrary.wiley.com/doi/10.1002/mds.26319/abstract Byline: Ina Schmitt, Oliver Kaut, Hassan Khazneh, Laura deBoni, Ashar Ahmad, Daniela Berg, Christine Klein, Holger Frohlich, Ullrich Wullner Keywords: Parkinson's disease; [alpha]-synuclein; DNA methylation; blood; l-dopa Abstract Background Increasing gene dosages of [alpha]-synuclein induce familial Parkinson's disease (PD); thus, the hypothesis has been put forward that regulation of gene expression, in particular altered [alpha]-synuclein gene methylation, might be associated with sporadic PD and could be used as a biological marker. Methods We performed a thorough analysis of [alpha]-synuclein methylation in bisulfite-treated DNA from peripheral blood of 490 sporadic PD patients and 485 healthy controls and in addition analyzed the effect of levodopa (l-dopa) on [alpha]-synuclein methylation and expression in cultured mononuclear cells. Results [alpha]-Synuclein was hypomethylated in sporadic PD patients, correlated with sex, age, and a polymorphism in the analyzed sequence stretch (rs3756063). [alpha]-Synuclein methylation separated healthy individuals from sporadic PD with a specificity of 74% (male) and 78% (female), respectively. [alpha]-Synuclein methylation was increased in sporadic PD patients with higher l-dopa dosage, and l-dopa specifically induced methylation of [alpha]-synuclein intron 1 in cultured mononuclear cells. Conclusions [alpha]-Synuclein methylation levels depended on disease status, sex, age, and the genotype of rs3756063. The pharmacological action of l-dopa was not limited to the dopamine precursor function but included epigenetic off-target effects. The hypomethylation of [alpha]-synuclein in sporadic PD patients' blood already observed in previous studies was probably underestimated because of effect of l-dopa, which was not known previously. The analysis of [alpha]-synuclein methylation can help to identify nonparkinsonian individuals with reasonable specificity, which offers a valuable tool for researchers and clinicians. [c] 2015 International Parkinson and Movement Disorder Society Article Note: Funding agencies: This work was supported by the Deutsche Parkinson Vereinigung (dPV); the Hans Tauber Stiftung; the Internationale Parkinson Fonds; and the pure consortium, in particular J. Wiltfang, the German Research Council (DFG: WU 184/9-1) and the Deutsche Zentrum fur neurodegenerative Erkrankungen (DZNE). This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking [Aetionomy [grant number 115568]). Relevant conflicts of interest/financial disclosures: The analysis of the methylation pattern of the [alpha]-synuclein gene for diagnostic purposes is part of the European patent application EP 2668291 A1. Full financial disclosures and author roles may be found in the online version of this article. Supporting information: Additional Supporting Information may be found in the online version of this article Additional Supporting Information may be found in the online version of this article at the publisher's web-site. CAPTION(S): Supplementary Information
    Keywords: Parkinson'S Disease ; Α‐Synuclein ; Dna Methylation ; Blood ; L ‐Dopa
    ISSN: 0885-3185
    E-ISSN: 1531-8257
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  • 9
    In: Bioinformatics, 2017, Vol. 33(22), pp.3558-3566
    Description: Discovery of clinically relevant disease sub-types is of prime importance in personalized medicine. Disease sub-type identification has in the past often been explored in an unsupervised machine learning paradigm which involves clustering of patients based on available-omics data, such as gene expression. A follow-up analysis involves determining the clinical relevance of the molecular sub-types such as that reflected by comparing their disease progressions. The above methodology, however, fails to guarantee the separability of the sub-types based on their subtype-specific survival curves. We propose a new algorithm, Survival-based Bayesian Clustering (SBC) which simultaneously clusters heterogeneous-omics and clinical end point data (time to event) in order to discover clinically relevant disease subtypes. For this purpose we formulate a novel Hierarchical Bayesian Graphical Model which combines a Dirichlet Process Gaussian Mixture Model with an Accelerated Failure Time model. In this way we make sure that patients are grouped in the same cluster only when they show similar characteristics with respect to molecular features across data types (e.g. gene expression, mi-RNA) as well as survival times. We extensively test our model in simulation studies and apply it to cancer patient data from the Breast Cancer dataset and The Cancer Genome Atlas repository. Notably, our method is not only able to find clinically relevant sub-groups, but is also able to predict cluster membership and survival on test data in a better way than other competing methods. Our R-code can be accessed as https://github.com/ashar799/SBC. ashar@bit.uni-bonn.de. Supplementary data are available at Bioinformatics online.
    Keywords: Algorithms ; Precision Medicine -- Methods;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
    E-ISSN: 13674811
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  • 10
    Language: English
    In: BMC bioinformatics, 30 April 2013, Vol.14, pp.144
    Description: Analysis and interpretation of biological networks is one of the primary goals of systems biology. In this context identification of sub-networks connecting sets of seed proteins or seed genes plays a crucial role. Given that no natural node and edge weighting scheme is available retrieval of a minimum size sub-graph leads to the classical Steiner tree problem, which is known to be NP-complete. Many approximate solutions have been published and theoretically analyzed in the computer science literature, but far less is known about their practical performance in the bioinformatics field. Here we conducted a systematic simulation study of four different approximate and one exact algorithms on a large human protein-protein interaction network with ~14,000 nodes and ~400,000 edges. Moreover, we devised an own algorithm to retrieve a sub-graph of merged Steiner trees. The application of our algorithms was demonstrated for two breast cancer signatures and a sub-network playing a role in male pattern baldness. We found a modified version of the shortest paths based approximation algorithm by Takahashi and Matsuyama to lead to accurate solutions, while at the same time being several orders of magnitude faster than the exact approach. Our devised algorithm for merged Steiner trees, which is a further development of the Takahashi and Matsuyama algorithm, proved to be useful for small seed lists. All our implemented methods are available in the R-package SteinerNet on CRAN (http://www.r-project.org) and as a supplement to this paper.
    Keywords: Algorithms ; Gene Regulatory Networks ; Protein Interaction Mapping
    E-ISSN: 1471-2105
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