Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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TLDR
A general framework for `soft' thresholding that assigns a connection weight to each gene pair is described and several node connectivity measures are introduced and provided empirical evidence that they can be important for predicting the biological significance of a gene.Abstract:
Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.read more
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Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions
Alexander Tolios,Alexander Tolios,J. De Las Rivas,Eivind Hovig,Patrick Trouillas,Andreas Scorilas,Thomas Mohr +6 more
TL;DR: The current review is aimed at providing guidance to existing methods with relevance to MDR research, and provides an overview on the identification of potential biomarkers using expression data and the prediction of treatment response by machine learning methods.
Journal ArticleDOI
Coexpression patterns define epigenetic regulators associated with neurological dysfunction.
Leandros Boukas,James M Havrilla,Peter Hickey,Aaron R. Quinlan,Hans T. Bjornsson,Kasper D. Hansen,Kasper D. Hansen +6 more
TL;DR: It is found that the majority of EM genes are very intolerant to loss-of-function variation, even when compared to the dosage sensitive transcription factors, and it is shown that regulatory regions near epigenetic regulators are genetically important for common neurological traits.
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Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles.
Kaizhong Xue,Sixiang Liang,Bingbing Yang,Dan Zhu,Yingying Xie,Wen Qin,Feng Liu,Yong Zhang,Chunshui Yu +8 more
TL;DR: Patients with MDD have reduced dReHo in brain areas associated with emotional and cognitive regulation, and these changes may be related to complex polygenetic and polypathway mechanisms.
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Integrative Analysis of "-Omics" Data Using Penalty Functions.
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GWAS and WGCNA uncover hub genes controlling salt tolerance in maize (Zea mays L.) seedlings.
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References
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Finding Groups in Data: An Introduction to Cluster Analysis
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