Research ArticleOpen AccessGoogle Scholar indexed
A Clique-Based Approach to the Identification of Common Gene Association Sub-Networks
Department of Mathematics and Computer Science, North Carolina Central University, Durham, USA
Department of Mathematics and Computer Science, North Carolina Central University, Durham, USA
Department of Mathematics and Computer Science, North Carolina Central University, Durham, USA
Department of Mathematics and Computer Science, North Carolina Central University, Durham, USA
- 1 Department of Mathematics and Computer Science, North Carolina Central University, Durham, USA
- 2 Department of Mathematics and Computer Science, North Carolina Central University, Durham, USA
- 3 Department of Mathematics and Computer Science, North Carolina Central University, Durham, USA
- 4 Department of Mathematics and Computer Science, North Carolina Central University, Durham, USA
Copy link · social · email
Abstract
We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.
KeywordsGraphical Lasso ModelQuadratic MaximizationSubgraph IsomorphismCliqueReplicator EquationGene Association Network
- J. Schafer and K. Strimmer, “An Empirical Bayes Approach to Inferring Large-Scale Gene Association Networks,” Bioinformatics, Vol. 21, No. 6, 2005, pp. 754764.
- P. Langfelder and S. Horvath, “WGCNA: An R Package for Weighted Correlation Network Analysis,” BMC Bioinformatics, Vol. 9, No. 1, 2008, p. 559. doi:10.1186/1471-2105-9-559
- N. Friedman, “Inferring Cellular Networks Using Probabilistic Graphical Models,” Science, Vol. 303, No. 5659, 2004, pp. 799-805. doi:10.1126/science.1094068
- M. K. S. Yeung, J. Tegnér and J. J. Collins, “Reverse Engineering Gene Networks Using Singular Value Decomposition and Robust Regression,” Proceedings of the National Academy of Sciences, Vol. 99, No. 9, 2002, pp. 6163-6168.
- C. Rangel, J. Angus, Z. Ghahramani, M. Lioumi, E. Sotheran, A. Gaiba, D. L. Wild and F. Falciani, “Modeling T-Cell Activation Using Gene Expression Profiling and State-Space Models,” Bioinformatics, Vol. 20, No. 9, 2004, pp. 1361-1372.
- J. Friedman, T. Hastie and R. Tibshirani, “Sparse Inverse Covariance Estimation with the Graphical Lasso,” Biostatistics, Vol. 9, No. 3, 2008, pp. 432-441.
- M. Pelillo, “Replicator Equations, Maximal Cliques, and Graph Isomorphism,” Neural Computation, Vol. 11, No. 8, 1999, pp. 1933-1955.
- R. Battiti and M. Protasi, “Reactive Local Search for the Maximum Clique Problem,” Algorithmica, Vol. 29, No. 4, 2001, pp. 610-637. doi:10.1007/s004530010074
- T. S. Motzkin and E. G. Straus, “Maxima for Graphs and a New Proof of a Theorem of Turán,” Canadian Journal of Mathematics, Vol. 17, 1965, pp. 533-540. doi:10.4153/CJM-1965-053-6
- N. Meinshausen and P. Bühlmann, “High-Dimensional Graphs and Variable Selection with the Lasso,” The Annals of Statistics, Vol. 34, No. 3, 2006, pp. 1436-1462. doi:10.1214/009053606000000281
- O. Banerjee, L. E. Ghaoui and A. d’Aspremont, “Model Selection through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data,” Journal of Machine Learning Research, Vol. 9, 2008, pp. 485516.
- A. Forsgren, P. E. Gill and M. H. Wright, “Interior Methods for Nonlinear Optimization,” SIAM Review, Vol. 44, No. 4, 2002, pp. 525-597. doi:10.1137/S0036144502414942
- J. Friedman, T. Hastie, H. Hofling and R. Tibshirani, “Pathwise Coordinate Optimization,” Annals of Applied Statistics, Vol. 1, No. 2, 2007, p. 302. doi:10.1214/07-AOAS131