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  • 11
    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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  • 12
    In: PLoS ONE, 2014, Vol.9(10)
    Description: Dependence measures and tests for independence have recently attracted a lot of attention, because they are the cornerstone of algorithms for network inference in probabilistic graphical models. Pearson's product moment correlation coefficient is still by far the most widely used statistic yet it is largely constrained to detecting linear relationships. In this work we provide an exact formula for the th nearest neighbor distance distribution of rank-transformed data. Based on that, we propose two novel tests for independence. An implementation of these tests, together with a general benchmark framework for independence testing, are freely available as a CRAN software package ( http://cran.r-project.org/web/packages/knnIndep ). In this paper we have benchmarked Pearson's correlation, Hoeffding's , dcor, Kraskov's estimator for mutual information, maximal information criterion and our two tests. We conclude that no particular method is generally superior to all other methods. However, dcor and Hoeffding's are the most powerful tests for many different types of dependence.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Physical Sciences
    E-ISSN: 1932-6203
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  • 13
    In: PLoS ONE, 2015, Vol.10(6)
    Description: Background The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics. Methods We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α , β)-k -Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone. Results Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 14
    In: PLoS ONE, 2014, Vol.9(2)
    Description: The recently proposed modified-compressive sensing (modified-CS), which utilizes the partially known support as prior knowledge, significantly improves the performance of recovering sparse signals. However, modified-CS depends heavily on the reliability of the known support. An important problem, which must be studied further, is the recoverability of modified-CS when the known support contains a number of errors. In this letter, we analyze the recoverability of modified-CS in a stochastic framework. A sufficient and necessary condition is established for exact recovery of a sparse signal. Utilizing this condition, the recovery probability that reflects the recoverability of modified-CS can be computed explicitly for a sparse signal with nonzero entries. Simulation experiments have been carried out to validate our theoretical results.
    Keywords: Research Article ; Computer Science ; Engineering ; Mathematics
    E-ISSN: 1932-6203
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  • 15
    In: PLoS ONE, 2014, Vol.9(3)
    Description: Time-course gene expression datasets, which record continuous biological processes of genes, have recently been used to predict gene function. However, only few positive genes can be obtained from annotation databases, such as gene ontology (GO). To obtain more useful information and effectively predict gene function, gene annotations are clustered together to form a learnable and effective learning system. In this paper, we propose a novel multi-instance hierarchical clustering (MIHC) method to establish a learning system by clustering GO and compare this method with other learning system establishment methods. Multi-label support vector machine classifier and multi-label K-nearest neighbor classifier are used to verify these methods in four yeast time-course gene expression datasets. The MIHC method shows good performance, which serves as a guide to annotators or refines the annotation in detail.
    Keywords: Research Article ; Biology ; Computer Science ; Mathematics
    E-ISSN: 1932-6203
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  • 16
    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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  • 17
    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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  • 18
    In: PLoS ONE, 2014, Vol.9(4)
    Description: We propose a new framework for rigorous robustness analysis of stochastic biochemical systems that is based on probabilistic model checking techniques. We adapt the general definition of robustness introduced by Kitano to the class of stochastic systems modelled as continuous time Markov Chains in order to extensively analyse and compare robustness of biological models with uncertain parameters. The framework utilises novel computational methods that enable to effectively evaluate the robustness of models with respect to quantitative temporal properties and parameters such as reaction rate constants and initial conditions. We have applied the framework to gene regulation as an example of a central biological mechanism where intrinsic and extrinsic stochasticity plays crucial role due to low numbers of DNA and RNA molecules. Using our methods we have obtained a comprehensive and precise analysis of stochastic dynamics under parameter uncertainty. Furthermore, we apply our framework to compare several variants of two-component signalling networks from the perspective of robustness with respect to intrinsic noise caused by low populations of signalling components. We have successfully extended previous studies performed on deterministic models (ODE) and showed that stochasticity may significantly affect obtained predictions. Our case studies demonstrate that the framework can provide deeper insight into the role of key parameters in maintaining the system functionality and thus it significantly contributes to formal methods in computational systems biology.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Physical Sciences
    E-ISSN: 1932-6203
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  • 19
    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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  • 20
    In: PLoS ONE, 2014, Vol.9(5)
    Description: The current gold-standard method for cancer safety assessment of drugs is a rodent two-year bioassay, which is associated with significant costs and requires testing a high number of animals over lifetime. Due to the absence of a comprehensive set of short-term assays predicting carcinogenicity, new approaches are currently being evaluated. One promising approach is toxicogenomics, which by virtue of genome-wide molecular profiling after compound treatment can lead to an increased mechanistic understanding, and potentially allow for the prediction of a carcinogenic potential via mathematical modeling. The latter typically involves the extraction of informative genes from omics datasets, which can be used to construct generalizable models allowing for the early classification of compounds with unknown carcinogenic potential. Here we formally describe and compare two novel methodologies for the reproducible extraction of characteristic mRNA signatures, which were employed to capture specific gene expression changes observed for nongenotoxic carcinogens. While the first method integrates multiple gene rankings, generated by diverse algorithms applied to data from different subsamplings of the training compounds, the second approach employs a statistical ratio for the identification of informative genes. Both methods were evaluated on a dataset obtained from the toxicogenomics database TG-GATEs to predict the outcome of a two-year bioassay based on profiles from 14-day treatments. Additionally, we applied our methods to datasets from previous studies and showed that the derived prediction models are on average more accurate than those built from the original signatures. The selected genes were mostly related to p53 signaling and to specific changes in anabolic processes or energy metabolism, which are typically observed in tumor cells. Among the genes most frequently incorporated into prediction models were Phlda3 , Cdkn1a , Akr7a3 , Ccng1 and Abcb4 .
    Keywords: Research Article ; Biology And Life Sciences ; Physical Sciences ; Computer And Information Sciences ; Medicine And Health Sciences
    E-ISSN: 1932-6203
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