1,721,040 research outputs found
Learning undirected graphical models from multiple datasets with the generalized non-rejection rate
No full textAbstractLearning graphical models from multiple datasets constitutes an appealing approach to learn transcriptional regulatory interactions from microarray data in the field of molecular biology. This has been approached both in a model based learning approach and in a model free learning approach where, in the latter, it is common practice to pool datasets produced under different experimental conditions. In this paper, we introduce a quantity called the generalized non-rejection rate which extends the non-rejection rate, introduced by [3], so as to explicitly keep into account the different graphical models representing distinct experimental conditions involved in the structure of the dataset produced in multiple experimental batches. We show that the generalized non-rejection rate allows one to learn the common edges occurring throughout all graphical models, making it specially suited to identify robust transcriptional interactions which are common to all the considered experiments. The generalized non-rejection rate is then applied to both synthetic and real data and shown to provide competitive performance with respect to other widely used methods
Structural learning of Gaussian graphical models from microarray data with p larger than n
No full textPackage "qp"
No full textPacchetto per il software statistico "R" scaricabile gratuitamente dal sito: http://cran.r-project.org/src/contrib/PACKAGES.html
Description: the q-order partial correlation graph search algorithm, q-partial, or qp, algorithm for short, is a robust procedure for structure learning of undirected Gaussian graphical Markov models from “small n, large p” data, that is, multivariate normal data coming from a number of random variables p larger than the number of multidimensional data points n as in the case of, e.g., microarray data
Package "qp"
No full textPacchetto per il software statistico "R" scaricabile gratuitamente dal sito: http://cran.r-project.org/src/contrib/PACKAGES.html
Description: the q-order partial correlation graph search algorithm, q-partial, or qp, algorithm for short, is a robust procedure for structure learning of undirected Gaussian graphical Markov models from “small n, large p” data, that is, multivariate normal data coming from a number of random variables p larger than the number of multidimensional data points n as in the case of, e.g., microarray data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Reverse engineering molecular regulatory networks from microarray data with qp-graphs
No full textReverse engineering bioinformatic procedures applied to high-throughput experimental data have become instrumental to generate new hypotheses about molecular regulatory mechanisms. This has been particularly the case for gene expression microarray data where a large number of statistical and computational methodologies have been developed in order to assist in building network models of transcriptional regulation. A major challenge faced by every different procedure is that the number of available samples n for estimating the network model is much smaller than the number of genes p forming the system under study. This compromises many of the assumptions on which the statistics of the methods rely, often leading to unstable performance figures. In this work we apply a recently developed novel methodology based in the so-called q-order lim-ited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data with p n. Using experimental and functional annotation data from Escherichia coli here we show how qp-graphs yield more stable performance figures than other state-of-the-art method
Inference of regulatory networks from microarray data with R and the Bioconductor package qpgraph
No full textRegulatory networks inferred from microarray data sets provide an estimated blueprint of the functional interactions taking place under the assayed experimental conditions. In each of these experiments, the gene expression pathway exerts a finely tuned control simultaneously over all genes relevant to the cellular state. This renders most pairs of those genes significantly correlated, and therefore, the challenge faced by every method that aims at inferring a molecular regulatory network from microarray data, lies in distinguishing direct from indirect interactions. A straightforward solution to this problem would be to move directly from bivariate to multivariate statistical approaches. However, the daunting dimension of typical microarray data sets, with a number of genes p several orders of magnitude larger than the number of samples n, precludes the application of standard multivariate techniques and confronts the biologist with sophisticated procedures that address this situation. We have introduced a new way to approach this problem in an intuitive manner, based on limited-order partial correlations, and in this chapter we illustrate this method through the R package qpgraph, which forms part of the Bioconductor project and is available at its Web site
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