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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, 2015, Vol.10(8)
    Description: Background The complexity of biological systems motivates us to use the underlying networks to provide deep understanding of disease etiology and the human diseases are viewed as perturbations of dynamic properties of networks. Control theory that deals with dynamic systems has been successfully used to capture systems-level knowledge in large amount of quantitative biological interactions. But from the perspective of system control, the ways by which multiple genetic factors jointly perturb a disease phenotype still remain. Results In this work, we combine tools from control theory and network science to address the diversified control paths in complex networks. Then the ways by which the disease genes perturb biological systems are identified and quantified by the control paths in a human regulatory network. Furthermore, as an application, prioritization of candidate genes is presented by use of control path analysis and gene ontology annotation for definition of similarities. We use leave-one-out cross-validation to evaluate the ability of finding the gene-disease relationship. Results have shown compatible performance with previous sophisticated works, especially in directed systems. Conclusions Our results inspire a deeper understanding of molecular mechanisms that drive pathological processes. Diversified control paths offer a basis for integrated intervention techniques which will ultimately lead to the development of novel therapeutic strategies.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 4
    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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  • 5
    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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  • 6
    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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  • 7
    Language: English
    In: BMC bioinformatics, 01 May 2012, Vol.13, pp.69
    Description: Stratification of patients according to their clinical prognosis is a desirable goal in cancer treatment in order to achieve a better personalized medicine. Reliable predictions on the basis of gene signatures could support medical doctors on selecting the right therapeutic strategy. However, during the last years the low reproducibility of many published gene signatures has been criticized. It has been suggested that incorporation of network or pathway information into prognostic biomarker discovery could improve prediction performance. In the meanwhile a large number of different approaches have been suggested for the same purpose. We found that on average incorporation of pathway information or protein interaction data did not significantly enhance prediction performance, but indeed greatly interpretability of gene signatures. Some methods (specifically network-based SVMs) could greatly enhance gene selection stability, but revealed only a comparably low prediction accuracy, whereas Reweighted Recursive Feature Elimination (RRFE) and average pathway expression led to very clearly interpretable signatures. In addition, average pathway expression, together with elastic net SVMs, showed the highest prediction performance here. The results indicated that no single algorithm to perform best with respect to all three categories in our study. Incorporating network of prior knowledge into gene selection methods in general did not significantly improve classification accuracy, but greatly interpretability of gene signatures compared to classical algorithms.
    Keywords: Algorithms ; Biomarkers -- Analysis ; Breast Neoplasms -- Genetics ; Gene Expression Profiling -- Methods
    E-ISSN: 1471-2105
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  • 8
    In: PLoS ONE, 2013, Vol.8(11)
    Description: Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci. Multiple testing methods in microarray data analysis aim at controlling both Type I and Type II error rates; however, real microarray data do not always fit their distribution assumptions. Smyth's ubiquitous parametric method, for example, inadequately accommodates violations of normality assumptions, resulting in inflated Type I error rates. The Significance Analysis of Microarrays, another widely used microarray data analysis method, is based on a permutation test and is robust to non-normally distributed data; however, the Significance Analysis of Microarrays method fold change criteria are problematic, and can critically alter the conclusion of a study, as a result of compositional changes of the control data set in the analysis. We propose a novel approach, combining resampling with empirical Bayes methods: the Resampling-based empirical Bayes Methods. This approach not only reduces false discovery rates for non-normally distributed microarray data, but it is also impervious to fold change threshold since no control data set selection is needed. Through simulation studies, sensitivities, specificities, total rejections, and false discovery rates are compared across the Smyth's parametric method, the Significance Analysis of Microarrays, and the Resampling-based empirical Bayes Methods. Differences in false discovery rates controls between each approach are illustrated through a preterm delivery methylation study. The results show that the Resampling-based empirical Bayes Methods offer significantly higher specificity and lower false discovery rates compared to Smyth's parametric method when data are not normally distributed. The Resampling-based empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes is large for both normally and non-normally distributed data. Finally, the Resampling-based empirical Bayes Methods are generalizable to next generation sequencing RNA-seq data analysis.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 9
    Language: English
    In: Immunogenetics, 2014, Vol.66(5), pp.287-297
    Description: Recently, evidence was provided for common familial occurrence of systemic mast cell activation disease (MCAD), i.e., mast cell disorders characterized by aberrant release of mast cell mediators and/or accumulation of pathological mast cells in potentially any tissue. Since there is accumulating evidence that epigenetic processes may have transgenerational consequences, the aim of the present study was to investigate by two different experimental approaches whether epigenetic effects may contribute to the familial occurrence of MCAD. (1) High throughput profiling of the methylation status of the genomic DNA in leukocytes from MCAD patients in comparison to healthy subjects revealed for the first time an association of MCAD with alterations in DNA methylation comprising genes encoding proteins crucially involved in DNA/RNA repair and processing, apoptosis, cell activity, and exocytosis/cell communication. A set of 195 differentially methylated CpG sites could be regarded as candidates for a MCAD signature at the methylation level of the DNA. (2) In a cohort of MCAD patients, a correlation between age at symptom onset and year of birth (reflecting different generations) was observed suggesting the presence of the phenomenon of anticipation. In conclusion, the present findings suggest that epigenetic processes could substantially contribute to the transgenerational transmission of MCAD.
    Keywords: Systemic mast cell activation disease ; Systemic mastocytosis ; Systemic mast cell activation syndrome ; Methylation ; Anticipation ; Epigenetics
    ISSN: 0093-7711
    E-ISSN: 1432-1211
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  • 10
    Language: English
    In: Bioinformatics (Oxford, England), 01 July 2012, Vol.28(13), pp.1714-20
    Description: There have been many successful experimental and bioinformatics efforts to elucidate transcription factor (TF)-target networks in several organisms. For many organisms, these annotations are complemented by miRNA-target networks of good quality. Attempts that use these networks in combination with gene expression data to draw conclusions on TF or miRNA activity are, however, still relatively sparse. In this study, we propose Bayesian inference of regulation of transcriptional activity (BIRTA) as a novel approach to infer both, TF and miRNA activities, from combined miRNA and mRNA expression data in a condition specific way. That means our model explains mRNA and miRNA expression for a specific experimental condition by the activities of certain miRNAs and TFs, hence allowing for differentiating between switches from active to inactive (negative switch) and inactive to active (positive switch) forms. Extensive simulations of our model reveal its good prediction performance in comparison to other approaches. Furthermore, the utility of BIRTA is demonstrated at the example of Escherichia coli data comparing aerobic and anaerobic growth conditions, and by human expression data from pancreas and ovarian cancer. The method is implemented in the R package birta, which is freely available for Bio-conductor (〉=2.10) on http://www.bioconductor.org/packages/release/bioc/html/birta.html.
    Keywords: Gene Expression Profiling ; Gene Regulatory Networks ; Micrornas -- Metabolism ; Transcription Factors -- Metabolism
    E-ISSN: 1367-4811
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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