Analysis of gene coexpression by B-spline based CoD estimation
EURASIP Journal on Bioinformatics and Systems Biology
Mutual Information Based Extrinsic Similarity for Microarray Analysis
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Journal of Biomedical Informatics
Global Similarity and Local Variance in Human Gene Coexpression Networks
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Method of regulatory network that can explore protein regulations for disease classification
Artificial Intelligence in Medicine
Identification and evaluation of functional modules in gene co-expression networks
RECOMB'06 Proceedings of the joint 2006 satellite conference on Systems biology and computational proteomics
Independent component analysis: Mining microarray data for fundamental human gene expression modules
Journal of Biomedical Informatics
A new method for identifying cancer-related gene association patterns
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Inference of the Genetic Network Regulating Lateral Root Initiation in Arabidopsis thaliana
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Gene expression data have become an instrumental resource in describing the molecular state associated with various cellular phenotypes and responses to environmental perturbations. The utility of expression profiling has been demonstrated in partitioning clinical states, predicting the class of unknown samples and in assigning putative functional roles to previously uncharacterized genes based on profile similarity. However, gene expression profiling has had only limited success in identifying therapeutic targets. This is partly due to the fact that current methods based on fold-change focus only on single genes in isolation, and thus cannot convey causal information. In this paper, we present a technique for analysis of expression data in a graph-theoretic framework that relies on associations between genes. We describe the global organization of these networks and biological correlates of their structure. We go on to present a novel technique for the molecular characterization of disparate cellular states that adds a new dimension to the fold-based methods and conclude with an example application to a human medulloblastoma dataset. Results: We have shown that expression networks generated from large model-organism expression datasets are scale-free and that the average clustering coefficient of these networks is several orders of magnitude higher than would be expected for similarly sized scale-free networks, suggesting an inherent hierarchical modularity similar to that previously identified in other biological networks. Furthermore, we have shown that these properties are robust with respect to the parameters of network construction. We have demonstrated an enrichment of genes having lethal knockout phenotypes in the high-degree (i.e. hub) nodes in networks generated from aggregate condition datasets; using process-focused Saccharomyces cerivisiae datasets we have demonstrated additional high-degree enrichments of condition-specific genes encoding proteins known to be involved in or important for the processes interrogated by the microarrays. These results demonstrate the utility of network analysis applied to expression data in identifying genes that are regulated in a state-specific manner. We concluded by showing that a sample application to a human clinical dataset prominently identified a known therapeutic target. Availability: Software implementing the methods for network generation presented in this paper is available for academic use by request from the authors in the form of compiled linux binary executables.