An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Biclustering Gene-Feature Matrices for Statistically Significant Dense Patterns
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Detecting disease-specific dysregulated pathways via analysis of clinical expression profiles
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Subnetwork state functions define dysregulated subnetworks in cancer
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Subnetwork state functions define dysregulated subnetworks in cancer
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
| Hi-index | 0.00 |
Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers However, existing methods formulate coordination between multiple genes through additive representation of their expression profiles and utilize greedy heuristics to identify dysregulated subnetworks, which may not be well suited to the potentially combinatorial nature of coordinate dysregulation Here, we propose a combinatorial formulation of coordinate dysregulation and decompose the resulting objective function to cast the problem as one of identifying subnetwork state functions that are indicative of phenotype Based on this formulation, we show that coordinate dysregulation of larger subnetworks can be bounded using simple statistics on smaller subnetworks We then use these bounds to devise an efficient algorithm, Crane, that can search the subnetwork space more effectively than simple greedy algorithms Comprehensive cross-classification experiments show that subnetworks identified by Crane significantly outperform those identified by greedy algorithms in predicting metastasis of colorectal cancer (CRC).