A pivotal allocation based algorithm for solving the label switching problem in Bayesian mixture models

<div><p>In Bayesian analysis of mixture models, the label switching problem occurs as a result of the posterior distribution being invariant to any permutation of cluster indices under symmetric priors. To solve this problem, we propose a novel relabeling algorithm and its variants by investigating an approximate posterior distribution of the latent allocation variables instead of dealing with the component parameters directly. We demonstrate that our relabeling algorithm can be formulated in a rigorous framework based on information theory. Under some circumstances, it is shown to resemble the classical Kullback-Leibler relabeling algorithm and include the recently proposed Equivalence Classes Representatives relabeling algorithm as a special case. Using simulation studies and real data examples, we illustrate the efficiency of our algorithm in dealing with various label switching phenomena. Supplemental materials for this article are available online.</p></div

Statistical power of phylo-HMM for evolutionarily conserved element detection-7

<p><b>Copyright information:</b></p><p>Taken from "Statistical power of phylo-HMM for evolutionarily conserved element detection"</p><p>http://www.biomedcentral.com/1471-2105/8/374</p><p>BMC Bioinformatics 2007;8():374-374.</p><p>Published online 5 Oct 2007</p><p>PMCID:PMC2194792.</p><p></p>etic tree. The different lines represent the different branches as illustrated in the legend. (B) Relationships for branches in the symmetric star-topology tree. The different lines correspond to the different numbers of genomes (n) represented by the tree

Statistical power of phylo-HMM for evolutionarily conserved element detection-5

<p><b>Copyright information:</b></p><p>Taken from "Statistical power of phylo-HMM for evolutionarily conserved element detection"</p><p>http://www.biomedcentral.com/1471-2105/8/374</p><p>BMC Bioinformatics 2007;8():374-374.</p><p>Published online 5 Oct 2007</p><p>PMCID:PMC2194792.</p><p></p>ample "0.0510" means = 0.05 and = 10. The points are their power at posterior probability threshold equal to 0.5. The point corresponding to the baseline (P0.25L50) is indicated as a green dot. The blue solid lines connect the points with the same , while the red dashed line connects the points with the same . Some of the points are highlighted by crosses. The green dotted line crosses show the 1-to-3quartile range. The black solid line crosses show the 95% bootstrap confidence interval of the median sensitivity and specificity

A Model-Based Method for Gene Dependency Measurement

<div><p>Many computational methods have been widely used to identify transcription regulatory interactions based on gene expression profiles. The selection of dependency measure is very important for successful regulatory network inference. In this paper, we develop a new method–DBoMM (Difference in BIC of Mixture Models)–for estimating dependency of gene by fitting the gene expression profiles into mixture Gaussian models. We show that DBoMM out-performs 4 other existing methods, including Kendall’s tau correlation (TAU), Pearson Correlation (COR), Euclidean distance (EUC) and Mutual information (MI) using <em>Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster</em>, <em>Arabidopsis thaliana</em> data and synthetic data. DBoMM can also identify condition-dependent regulatory interactions and is robust to noisy data. Of the 741 <em>Escherichia coli</em> regulatory interactions inferred by DBoMM at a 60% true positive rate, 65 are previously known interactions and 676 are novel predictions. To validate the new prediction, the promoter sequences of target genes regulated by the same transcription factors were analyzed and significant motifs were identified.</p> </div

Exhaustive Cross-Linking Search with Protein Feedback

Improving the sensitivity of protein–protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/β mass detection scheme and establishes protein–peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links

DBoMM can identify the conditional dependent regulatory interactions between two genes.

<p>The experimental conditions are classified into 6 different clusters based on the expression profiles of two genes (<i>lexA</i> and <i>recA</i>). Cn represents the index of the cluster.</p

A comparison of different methods using PR-curve.

<p>(a). <i>E.coli</i> dataset and the reference network from RegulonDB; (b). <i>Yeast</i> datset and the reference network from YEASTRACT; (c). <i>Drosophila</i> dataset and the reference network from DroID; (d). <i>Arabidopsis</i> datset and the reference network from AGRIS. X axis: recall; Y axis: precision. In general, DBoMM out-performs other 4 methods using various datasets.</p

The distributions of different similarity scores.

<p>The distributions of different similarity scores.</p

Motifs detected for TF and .

<p>(a). The regulatory motif detected in the promoters of the 19 inferred target operons(upper) compared to the motif identified in PRODORIC. (b). The regulatory motif detected in the promoters of 8 inferred target operons(upper) compared to the motif identified in PRODORIC(lower).</p

Exhaustive Cross-Linking Search with Protein Feedback

Improving the sensitivity of protein–protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/β mass detection scheme and establishes protein–peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links