Kenneth Bryan

Application of Simulated Annealing to the Biclustering of Gene Expression Data

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 Churchs 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

Biclustering of expression data using simulated annealing

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 Churchs 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.

Unsupervised retrieval of attack profiles in collaborative recommender systems

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.

Post-Transcriptional Dysregulation by miRNAs Is Implicated in the Pathogenesis of Gastrointestinal Stromal Tumor [GIST]

In contrast to adult mutant gastrointestinal stromal tumors [GISTs], pediatric/wild-type GISTs remain poorly understood overall, given their lack of oncogenic activating tyrosine kinase mutations. These GISTs, with a predilection for gastric origin in female patients, show limited response to therapy with tyrosine kinase inhibitors and generally pursue a more indolent course, but still may prove fatal. Defective cellular respiration appears to underpin tumor development in these wild-type cases, which as a group lack expression of succinate dehydrogenase [SDH] B, a surrogate marker for respiratory chain metabolism. Yet, only a small subset of the wild-type tumors show mutations in the genes coding for the SDH subunits [SDHx]. To explore additional pathogenetic mechanisms in these wild-type GISTs, we elected to investigate post-transcriptional regulation of these tumors by conducting microRNA (miRNA) profiling of a mixed cohort of 73 cases including 18 gastric pediatric wild-type, 25 (20 gastric, 4 small bowel and 1 retroperitoneal) adult wild-type GISTs and 30 gastric adult mutant GISTs. By this approach we have identified distinct signatures for GIST subtypes which correlate tightly with clinico-pathological parameters. A cluster of miRNAs on 14q32 show strikingly different expression patterns amongst GISTs, a finding which appears to be explained at least in part by differential allelic methylation of this imprinted region. Small bowel and retroperitoneal wild-type GISTs segregate with adult mutant GISTs and express SDHB, while adult wild-type gastric GISTs are dispersed amongst adult mutant and pediatric wild-type cases, clustering in this situation on the basis of SDHB expression. Interestingly, global methylation analysis has recently similarly demonstrated that these wild-type, SDHB-immunonegative tumors show a distinct pattern compared with KIT and PDGFRA mutant tumors, which as a rule do express SDHB. All cases with Carney triad within our cohort cluster together tightly.

MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model.

Bovine mastitis is an inflammation-driven disease of the bovine mammary gland that costs the global dairy industry several billion dollars per year. Because disease susceptibility is a multifactorial complex phenotype, an integrative biology approach is required to dissect the molecular networks involved. Here, we report such an approach using next-generation sequencing combined with advanced network and pathway biology methods to simultaneously profile mRNA and miRNA expression at multiple time points (0, 12, 24, 36 and 48 hr) in milk and blood FACS-isolated CD14+ monocytes from animals infected in vivo with Streptococcus uberis. More than 3700 differentially expressed (DE) genes were identified in milk-isolated monocytes (MIMs), a key immune cell recruited to the site of infection during mastitis. Upregulated genes were significantly enriched for inflammatory pathways, whereas downregulated genes were enriched for nonglycolytic metabolic pathways. Monocyte transcriptional changes in the blood, however, were more subtle but highlighted the impact of this infection systemically. Genes upregulated in blood-isolated monocytes (BIMs) showed a significant association with interferon and chemokine signaling. Furthermore, 26 miRNAs were DE in MIMs and three were DE in BIMs. Pathway analysis revealed that predicted targets of downregulated miRNAs were highly enriched for roles in innate immunity (FDR < 3.4E−8), particularly TLR signaling, whereas upregulated miRNAs preferentially targeted genes involved in metabolism. We conclude that during S. uberis infection miRNAs are key amplifiers of monocyte inflammatory response networks and repressors of several metabolic pathways.

Bottom-Up Biclustering of Expression Data

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

BALBOA: Extending Bicluster Analysis to Classify ORFs using Expression Data

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.

HiQuant: Rapid Postquantification Analysis of Large-Scale MS-Generated Proteomics Data

Recent advances in mass-spectrometry-based proteomics are now facilitating ambitious large-scale investigations of the spatial and temporal dynamics of the proteome; however, the increasing size and complexity of these data sets is overwhelming current downstream computational methods, specifically those that support the postquantification analysis pipeline. Here we present HiQuant, a novel application that enables the design and execution of a postquantification workflow, including common data-processing steps, such as assay normalization and grouping, and experimental replicate quality control and statistical analysis. HiQuant also enables the interpretation of results generated from large-scale data sets by supporting interactive heatmap analysis and also the direct export to Cytoscape and Gephi, two leading network analysis platforms. HiQuant may be run via a user-friendly graphical interface and also supports complete one-touch automation via a command-line mode. We evaluate HiQuants performance by analyzing a large-scale, complex interactome mapping data set and demonstrate a 200-fold improvement in the execution time over current methods. We also demonstrate HiQuants general utility by analyzing proteome-wide quantification data generated from both a large-scale public tyrosine kinase siRNA knock-down study and an in-house investigation into the temporal dynamics of the KSR1 and KSR2 interactomes. Download HiQuant, sample data sets, and supporting documentation at http://hiquant.primesdb.eu .

Parallel integration of heterogeneous genome-wide data sources

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.

Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks [version 1; referees: 1 approved]

Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. AVAILABILITY CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).