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  • 1
    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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  • 2
    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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  • 3
    In: Bioinformatics, 2014, Vol. 30(9), pp.1325-1326
    Description: In the past years, there has been a growing interest in methods that incorporate network information into classification algorithms for biomarker signature discovery in personalized medicine. The general hope is that this way the typical low reproducibility of signatures, together with the difficulty to link them to biological knowledge, can be addressed. Complementary to these efforts, there is an increasing interest in integrating different data entities (e.g. gene and miRNA expressions) into comprehensive models. To our knowledge, R-package netClass is the first software that addresses both, network and data integration. Besides several published approaches for network integration, it specifically contains our recently published stSVM method, which allows for additional integration of gene and miRNA expression data into one predictive classifier. Availability: netClass is available on http://sourceforge.net/p/netclassr and CRAN ( http://cran.r-project.org ). Contact: yupeng.cun@gmail.com
    Keywords: Biology;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 4
    In: Bioinformatics, 2011, Vol. 27(2), pp.238-244
    Description: Motivation: Targeted interventions in combination with the measurement of secondary effects can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades. Nested effect models (NEMs) have been introduced as a statistical approach to estimate the upstream signal flow from downstream nested subset structure of perturbation effects. The method was substantially extended later on by several authors and successfully applied to various datasets. The connection of NEMs to Bayesian Networks and factor graph models has been highlighted. Here, we introduce a computationally attractive extension of NEMs that enables the analysis of perturbation time series data, hence allowing to discriminate between direct and indirect signaling and to resolve feedback loops. The implementation (R and C) is part of the Supplement to this article. 〈p〉〈bold〉Contact:〈/bold〉 〈email〉frohlich@bit.uni-bonn.de〈/email〉〈/p〉 are available at online.
    Keywords: Nanoelectromechanical Systems ; Computation ; Mathematical Models ; Upstream ; Perturbation Methods ; Feedback ; Bioinformatics ; Cascades ; Life and Medical Sciences (Ci);
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 5
    In: Bioinformatics, 2013, Vol. 29(12), pp.1534-1540
    Description: Motivation: As RNA interference is becoming a standard method for targeted gene perturbation, computational approaches to reverse engineer parts of biological networks based on measurable effects of RNAi become increasingly relevant. The vast majority of these methods use gene expression data, but little attention has been paid so far to other data types. : Here we present a method, which can infer gene networks from high-dimensional phenotypic perturbation effects on single cells recorded by time-lapse microscopy. We use data from the Mitocheck project to extract multiple shape, intensity and texture features at each frame. Features from different cells and movies are then aligned along the cell cycle time. Subsequently we use Dynamic Nested Effects Models (dynoNEMs) to estimate parts of the network structure between perturbed genes via a Markov Chain Monte Carlo approach. Our simulation results indicate a high reconstruction quality of this method. A reconstruction based on 22 gene knock downs yielded a network, where all edges could be explained via the biological literature. : The implementation of dynoNEMs is part of the Bioconductor R-package . 〈p〉〈bold〉Contact:〈/bold〉 〈email〉frohlich@bit.uni-bonn.de〈/email〉〈/p〉 are available at online.
    Keywords: Gene Expression ; Computer Programs ; Learning ; Data Processing ; Cell Cycle ; Microscopy ; RNA-Mediated Interference ; Bioinformatics ; Computer Applications ; Imaging ; Internet ; Bioinformatics & Computer Applications ; Methods;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 6
    Language: English
    In: Bioinformatics (Oxford, England), 15 May 2011, Vol.27(10), pp.1442-3
    Description: Prognostic and diagnostic biomarker discovery is one of the key issues for a successful stratification of patients according to clinical risk factors. For this purpose, statistical classification methods, such as support vector machines (SVM), are frequently used tools. Different groups have recently shown that the usage of prior biological knowledge significantly improves the classification results in terms of accuracy as well as reproducibility and interpretability of gene lists. Here, we introduce pathClass, a collection of different SVM-based classification methods for improved gene selection and classfication performance. The methods contained in pathClass do not merely rely on gene expression data but also exploit the information that is carried in gene network data. pathClass is open source and freely available as an R-Package on the CRAN repository at http://cran.r-project.org.
    Keywords: Algorithms ; Gene Regulatory Networks ; Biomarkers -- Analysis ; Gene Expression Profiling -- Methods ; Neoplasms -- Diagnosis
    ISSN: 13674803
    E-ISSN: 1367-4811
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  • 7
    In: Bioinformatics, 2017, Vol. 33(21), pp.3445-3453
    Description: Integration of metabolic networks with '-omics' data has been a subject of recent research in order to better understand the behaviour of such networks with respect to differences between biological and clinical phenotypes. Under the conditions of steady state of the reaction network and the non-negativity of fluxes, metabolic networks can be algebraically decomposed into a set of sub-pathways often referred to as extreme currents (ECs). Our objective is to find the statistical association of such sub-pathways with given clinical outcomes, resulting in a particular instance of a self-contained gene set analysis method. In this direction, we propose a method based on sparse group lasso (SGL) to identify phenotype associated ECs based on gene expression data. SGL selects a sparse set of feature groups and also introduces sparsity within each group. Features in our model are clusters of ECs, and feature groups are defined based on correlations among these features. We apply our method to metabolic networks from KEGG database and study the association of network features to prostate cancer (where the outcome is tumor and normal, respectively) as well as glioblastoma multiforme (where the outcome is survival time). In addition, simulations show the superior performance of our method compared to global test, which is an existing self-contained gene set analysis method. R code (compatible with version 3.2.5) is available from http://www.abi.bit.uni-bonn.de/index.php?id=17. samal@combine.rwth-aachen.de or frohlich@bit.uni-bonn.de. Supplementary data are available at Bioinformatics online.
    Keywords: Algorithms ; Metabolic Networks and Pathways ; Phenotype ; Computational Biology -- Methods;
    ISSN: 1367-4803
    ISSN: 13674811
    E-ISSN: 1460-2059
    E-ISSN: 13674811
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  • 8
    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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  • 9
    In: Bioinformatics, 2010, Vol. 26(18), pp.i596-i602
    Description: Motivation: Network modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays. Signalling dynamics is modelled using active and passive states for each protein at each timepoint. A fixed signal propagation scheme generates a set of possible state transitions on a discrete timescale for a given network hypothesis, reducing the number of theoretically reachable states. A likelihood score is proposed, describing the probability of measurements given the states of the proteins over time. The optimal sequence of state transitions is found via a hidden Markov model and network structure search is performed using a genetic algorithm that optimizes the overall likelihood of a population of candidate networks. Our method shows increased performance compared with two different dynamical Bayesian network approaches. For our real data, we were able to find several known signalling cascades from the ERBB signalling pathway. Dynamic deterministic effects propagation networks is implemented in the R programming language and available at 〈p〉〈bold〉Contact:〈/bold〉 〈email〉c.bender@dkfz.de〈/email〉〈/p〉
    Keywords: Learning ; Data Processing ; Bayesian Analysis ; Algorithms ; Erbb Protein ; Computer Programs ; Tumor Cell Lines ; Hidden Markov Models ; Protein Arrays ; Breast Cancer ; Language ; Bioinformatics ; Signal Transduction ; Bioinformatics & Computer Applications;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 10
    In: Bioinformatics, 2010, Vol. 26(17), pp.2136-2144
    Description: Motivation: One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures. We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Googleʼs PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes. The R code of the proposed algorithm is given in . ; are available at online.
    Keywords: Biology;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
    E-ISSN: 13674811
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