UNLABELLED: : The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most 'rewired' nodes across many network states. AVAILABILITY AND IMPLEMENTATION: DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Heterogeneous genome-wide data sources capture information on various aspects of complex biological systems. For instance, transcriptome, interactome and phenome-level information may be derived from mRNA expression data, protein-protein interaction networks, and biomedical literature corpora. Each source provides a distinct ldquoviewrdquo of the same domain, but potentially encodes different biologically-relevant patterns. Effective integration of such views can provide a richer, more informative model of an organismpsilas functional modules than that produced on a single view alone. Existing machine learning strategies for information fusion largely focus on the production of a consensus model that reflects patterns shared between views. However, the information provided by different views may not always be easily reconciled, due to the incomplete nature of the data, or the fact that some patterns will be present in one view but not in another. To address this problem, we present the Parallel Integration Clustering Algorithm (PICA), a novel cluster analysis approach which supports the simultaneous integration of information from two or more sources. The resulting model preserves patterns that are unique to individual views, as well as those common to all views. We demonstrate the effectiveness of PICA in identifying significant patterns corresponding to functional groupings, when applied to three genome-wide datasets.

Trust, reputation and recommendation are key components of successful e-commerce systems. However, e-commerce systems are also vulnerable in this respect because there are opportunities for sellers to gain advantage through manipulation of reputation and recommendation. One such vulnerability is the use of fraudulent user profiles to boost (or damage) the ratings of items in an online recommender system. In this paper we cast this problem as a problem of detecting anomalous structure in network analysis and propose a novel mechanism for detecting this anomalous structure. We present an evaluation that shows that this approach is effective at uncovering the types of recommender systems attack described in the literature.

BACKGROUND: Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or features, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data. RESULTS: In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations. CONCLUSION: Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.

Microarrays have the capacity to measure the expressions of thousands of genes in parallel over many experimental samples. The unsupervised technique of bicluster analysis has been employed previously to uncover gene expression correlations over subsets of samples with the aim of modelling the natural gene functional classes. However the bicluster model also has the potential to shed light on the functions of unannotated open reading frames (ORFs). This aspect of biclustering has been under-explored. In this work we illustrate how the bicluster representation of expression data may be extended to enable putative functional classification of unannotated ORFs. We develop an ORF annotation approach, referred to as BALBOA, in which classifiers are constructed from the class specific expression patterns discovered by bicluster analysis. We demonstrate the efficacy of this approach via cross validation and carry out a comparative evaluation with kNN classification across three yeast expression datasets. Finally, we assign putative functions to unannotated ORFs and attempt to corroborate the best supported annotations with external experimental and protein sequence information.

In a gene expression data matrix a bicluster is a sub-matrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The premise behind biclustering is that even related genes may only be expressed in a synchronized fashion over certain conditions. Conventional clustering groups over all features and may not capture these local relationships. Biclustering has the potential to retrieve these local signals and also to model overlapping groups of genes. These factors allow better representation of the natural state of functional modules in the cell. The mean squared residue is a popular measure of bicluster quality. One drawback however is that it is biased toward flat biclusters with low row variance. In this paper we introduce an improved bicluster score that removes this bias and promotes the discovery the most significant biclusters in the dataset. We employ this score within a new biclustering approach based on the bottom-up search strategy. We believe that the bottom-up search approach better models the underlying functional modules of the gene expression dataset. We evaluate our new score against the mean squared residue score using a yeast cell cycle expression dataset. We then carry out a comparative analysis of our biclustering technique against previously published clustering and biclustering approaches. Lastly, we use the biclusters discovered by our method to attempt to putatively annotate unclassified genes

In a gene expression data matrix, a bicluster is a submatrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The problem of locating the most significant bicluster has been shown to be NP-complete. Heuristic approaches such as Cheng and Church's greedy node deletion algorithm have been previously employed. It is to be expected that stochastic search techniques such as evolutionary algorithms or simulated annealing might improve upon such greedy techniques. In this paper we show that an approach based on simulated annealing is well suited to this problem, and we present a comparative evaluation of simulated annealing and node deletion on a variety of datasets. We show that simulated annealing discovers more significant biclusters in many cases. Furthermore, we also test the ability of our technique to locate biologically verifiable biclusters within an annotated set of genes.

In a gene expression data matrix a bicluster is a grouping of a subset of genes and a subset of conditions which show correlating levels of expression activity. The difficulty of finding significant biclusters in gene expression data grows exponentially with the size of the dataset and heuristic approaches such as Cheng and Church's greedy node deletion algorithm are required. It is to be expected that stochastic search techniques such as genetic algorithms or simulated annealing might produce better solutions than greedy search. In this paper we show that a simulated annealing approach is well suited to this problem and we present a comparative evaluation of simulated annealing and node deletion on a variety of datasets. We show that simulated annealing discovers more significant biclusters in many cases.