GeneRank: Using search engine technology for the analysis of microarray experiments

Morrison, Julie; Breitling, Rainer; Higham, Desmond J.; Gilbert, David

doi:10.1186/1471-2105-6-233

Cited by 227 publications

(132 citation statements)

References 17 publications

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“…PageRank centrality is based on the importance (specifically the PageRank centrality) of a node’s neighbors. A node with high PageRank centrality may not necessarily maintain many strong, functional connections (i.e., have high degree centrality) but maintains functional connections with neighboring nodes that are highly central to the network (Boldi et al, 2009; Morrison et al, 2005); it should be noted that similar centrality measures (e.g., eigenvector centrality, betweenness centrality) may also similarly examine this property but we chose PageRank centrality (vs. eigenvector centrality or betweenness centrality) because it has been shown to be more sensitive to age-related effects (Khazaee et al, 2016; Zuo et al, 2012). The use of these two graph metrics allowed us to examine the three previously described ways a module may become central to a network, which, again, may be through (a) maintaining strong functional connections (high degree centrality but low PageRank centrality), (b) becoming more reliant on highly central nodes in other modules (high PageRank centrality but low degree centrality), or (c) both (high degree centrality and PageRank centrality).…”

Section: 0 Introductionmentioning

confidence: 99%

Functional modular architecture underlying attentional control in aging

et al. 2017

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Previous research suggests that age-related differences in attention reflect the interaction of top-down and bottom-up processes, but the cognitive and neural mechanisms underlying this interaction remain an active area of research. Here, within a sample of community-dwelling adults 19–78 years of age, we used diffusion reaction time (RT) modeling and multivariate functional connectivity to investigate the behavioral components and whole-brain functional networks, respectively, underlying bottom-up and top-down attentional processes during conjunction visual search. During functional MRI scanning, participants completed a conjunction visual search task in which each display contained one item that was larger than the other items (i.e., a size singleton) but was not informative regarding target identity. This design allowed us to examine in the RT components and functional network measures the influence of (a) additional bottom-up guidance when the target served as the size singleton, relative to when the distractor served as the size singleton (i.e., size singleton effect) and (b) top-down processes during target detection (i.e., target detection effect; target present vs. absent trials). We found that the size singleton effect (i.e., increased bottom-up guidance) was associated with RT components related to decision and nondecision processes, but these effects did not vary with age. Also, a modularity analysis revealed that frontoparietal module connectivity was important for both the size singleton and target detection effects, but this module became central to the networks through different mechanisms for each effect. Lastly, participants 42 years of age and older, in service of the target detection effect, relied more on between-frontoparietal module connections. Our results further elucidate mechanisms through which frontoparietal regions support attentional control and how these mechanisms vary in relation to adult age.

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Section: 0 Introductionmentioning

confidence: 99%

Functional modular architecture underlying attentional control in aging

et al. 2017

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“…NetBC uses the GeneRank algorithm 51 to assign weights to genes. Suppose interactions between m genes are encoded by .…”

Section: Methodsmentioning

confidence: 99%

Network-aided Bi-Clustering for discovering cancer subtypes

Wang

2017

Sci Rep

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Bi-clustering is a widely used data mining technique for analyzing gene expression data. It simultaneously groups genes and samples of an input gene expression data matrix to discover bi-clusters that relevant samples exhibit similar gene expression profiles over a subset of genes. The discovered bi-clusters bring insights for categorization of cancer subtypes, gene treatments and others. Most existing bi-clustering approaches can only enumerate bi-clusters with constant values. Gene interaction networks can help to understand the pattern of cancer subtypes, but they are rarely integrated with gene expression data for exploring cancer subtypes. In this paper, we propose a novel method called Network-aided Bi-Clustering (NetBC). NetBC assigns weights to genes based on the structure of gene interaction network, and it iteratively optimizes sum-squared residue to obtain the row and column indicative matrices of bi-clusters by matrix factorization. NetBC can not only efficiently discover bi-clusters with constant values, but also bi-clusters with coherent trends. Empirical study on large-scale cancer gene expression datasets demonstrates that NetBC can more accurately discover cancer subtypes than other related algorithms.

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“…Another case study on breast cancer metastasis prediction also showed that several most popular pathway or network-based approaches [71-73] even cannot compete with a simple, single genes based classifier in an extensive and critical comparison [74]. In Figure 4, we showed an intuitional comparison of the results respectively produced by conventional network-based gene ranking (similar to PageRank algorithm used by Google) [76], 2D hierarchical clustering [77] and ACOR [67-69], for analyzing a BRCA-related protein interaction network [68]. From this comparison, we can see directly that only ACOR approach can reorder the adjacency matrix of the BRCA-related protein network to a meaningful pattern, which has many clusters closely overlapped.…”

Section: Network Analysis For Complex Protein Networkmentioning

confidence: 99%

Pathway and network analysis in proteomics

Hasan

Chen

2014

Journal of Theoretical Biology

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Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results being sensitive to data preparation methods, sample condition, instrument types, and analytical method. To address this challenge in Proteomics data analysis, we review common approaches developed to incorporate biological function and network topological information. We categorize existing tools into four categories: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We review the general application potential of these tools to Proteomics. In addition, we also review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.

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GeneRank: Using search engine technology for the analysis of microarray experiments

Cited by 227 publications

References 17 publications

Functional modular architecture underlying attentional control in aging

Functional modular architecture underlying attentional control in aging

Network-aided Bi-Clustering for discovering cancer subtypes

Pathway and network analysis in proteomics

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