Application of new probabilistic graphical models in the genetic regulatory networks studies

This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an Independence Graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian Network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions is preserved. Additionally, we found two key limitations in the prediction of genetic regulatory networks using DNA microarray data, the first is the sufficiency of sample size and the second is the complexity of network structures may not be captured without additional data at the protein level. Those limitations are present in all prediction methods which used only DNA microarray data.Comment: 38 pages, 3 figure

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.</p> <p>Results</p> <p>We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC) devoted to BN structure learning.</p> <p>Conclusion</p> <p>We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.</p

An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning

International audienceWe present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. It is based on a subroutine called HPC, that combines ideas from incremental and divide-and-conquer constraint-based methods to learn the parents and children of a target variable. We conduct an experimental comparison of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning, on several benchmarks with various data sizes. Our extensive experiments show that H2PC outperforms MMHC both in terms of goodness of fit to new data and in terms of the quality of the network structure itself, which is closer to the true dependence structure of the data. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available

Association analyses of the MAS-QTL data set using grammar, principal components and Bayesian network methodologies

<p>Abstract</p> <p>Background</p> <p>It has been shown that if genetic relationships among individuals are not taken into account for genome wide association studies, this may lead to false positives. To address this problem, we used Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification analyses. To account for linkage disequilibrium among the significant markers, principal components loadings obtained from top markers can be included as covariates. Estimation of Bayesian networks may also be useful to investigate linkage disequilibrium among SNPs and their relation with environmental variables.</p> <p>For the quantitative trait we first estimated residuals while taking polygenic effects into account. We then used a single SNP approach to detect the most significant SNPs based on the residuals and applied principal component regression to take linkage disequilibrium among these SNPs into account. For the categorical trait we used principal component stratification methodology to account for background effects. For correction of linkage disequilibrium we used principal component logit regression. Bayesian networks were estimated to investigate relationship among SNPs.</p> <p>Results</p> <p>Using the Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification approach we detected around 100 significant SNPs for the quantitative trait (p<0.05 with 1000 permutations) and 109 significant (p<0.0006 with local FDR correction) SNPs for the categorical trait. With additional principal component regression we reduced the list to 16 and 50 SNPs for the quantitative and categorical trait, respectively.</p> <p>Conclusions</p> <p>GRAMMAR could efficiently incorporate the information regarding random genetic effects. Principal component stratification should be cautiously used with stringent multiple hypothesis testing correction to correct for ancestral stratification and association analyses for binary traits when there are systematic genetic effects such as half sib family structures. Bayesian networks are useful to investigate relationships among SNPs and environmental variables.</p

Pacifism in Fin-de-Siècle Austria: The Politics and Limits of Peace Activism

The late Habsburg Monarchy produced two of the most renowned peace activists of their day: Bertha von Suttner and Alfred Fried. In comparison to these two Nobel Peace laureates, the main association of Austro-pacifism – the Österreichische Friedensgesellschaft (ÖFG) – is less well known. The article concentrates on this organization, which had been founded in 1891, and it draws attention to the political and intellectual environment in which it operated. The ÖFG originated in the milieu of Austro-German liberalism, but had an ambivalent rapport with liberal politics. The Austro-pacifists' focus on supranational principles and dynastic loyalty sat uneasily with the national dimensions of Cisleithanian politics. The obstacles encountered by the ÖFG illustrate wider aspects of the political culture of fin-de-siècle Austria, ranging from the question of militarism in Austrian society to the challenges created by socialist and nationalist movements. As a whole, the article highlights the inherent limitations of Austro-pacifism, as reflected in its quest for respectability and its acceptance of the social and political order

Exploring the impact of cumulative testing on academic performance of undergraduate students in Spain

The final publication is available at Springer via http://dx.doi.org/10.1007/s11092-014-9208-zFrequent testing provides opportunities for students to receive regular feedback and to increase their motivation. It also provides the instructor with valuable information on how course progresses, thus making it possible to solve the problems encountered before it is too late. Frequent tests with noncumulative contents have been widely analysed in the literature with inconclusive results. However, cumulative testing methods have hardly been reported in higher education courses. This paper analyses the effect of applying an assessment method based on frequent and cumulative tests on student performance. Our results show that, when applied to a microeconomics course, students who were assessed by a frequent, cumulative testing approach largely outperformed those assessed with a single final exam.Doménech I De Soria, J.; Blázquez Soriano, MD.; De La Poza, E.; Muñoz Miquel, A. (2015). 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Dordrecht: Springer Netherlands.Eikner, AE, & Montondon, L. (2001). Evidence on factors associated with success in intermediate accounting I. Accounting Educators’ Journal 13.Emerson, TLN, & Mencken, KD. (2011). Homework to require or not? online graded homework and student achievement Perspectives on Economic Education Research 7.Fulkerson, F, & Martin, G. (1981). Effects of exam frequency on student performance, evaluations of instructor, and test anxiety. Teaching of Psychology, 8, 90–93.Furnham, A, & Chamorro-Premuzic, T. (2005). Individual differences and beliefs concerning preference for university assessment methods. Journal of Applied Social Psychology, 35, 1968–1994.Gibbs, G, & Simpson, C. (2005). Conditions under which assessment supports students’ learning Learning and Teaching in Higher Education 1 (August 5, 2011)3–31.Haberyan, KA. (2003). Do weekly quizzes improve student performance on general biology exams?. 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Student Employment: Linking College and the Workplace

The focus of National Student Employment Association (formerly the National Association of Student Employment Administrators, or NASEA) publications has always been on students in transition. From the freshman moving from high school to higher education, to the senior attempting the transition to professional employment and financial independence, we always have explored how students can better accomplish these linking experiences. Student employment is a hybrid, serving as a bridge between work and school, and ultimately, a link between school and full-time work. Student employment links elements of financial aid, career development, academic learning, experiential education, and personal development. Student employment, in all of these ways, is a bridge, moving the student from point A to point B. Because of this variety, any publication on student employment must necessarily speak to diverse themes. We have organized this publication in four sections: an introduction followed by three themed sections.https://digitalcommons.brockport.edu/bookshelf/1000/thumbnail.jp

A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid detection of Bacillus spores and identification of Bacillus species

Background The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS. Results We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers) to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features. Conclusions This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA). The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA