Introduction to algorithms
Approximation algorithms for NP-hard problems
Connected set cover problem and its applications
AAIM'06 Proceedings of the Second international conference on Algorithmic Aspects in Information and Management
Evaluating Between-Pathway Models with Expression Data
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Heuristic algorithms in computational molecular biology
Journal of Computer and System Sciences
Subnetwork state functions define dysregulated subnetworks in cancer
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Simultaneous identification of causal genes and dys-regulated pathways in complex diseases
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Algorithms for detecting significantly mutated pathways in cancer
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Efficient algorithms for extracting biological key pathways with global constraints
Proceedings of the 14th annual conference on Genetic and evolutionary computation
NABEECO: biological network alignment with bee colony optimization algorithm
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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We present a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. These subnetworks provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention. Our method uses microarray gene expression profiles derived in clinical case-control studies to identify genes significantly dysregulated in disease specimens, combined with protein interaction data to identify connected sets of genes. Our core algorithm searches for minimal connected subnetworks in which the number of dysregulated genes in each diseased sample exceeds a given threshold. We have applied the method in a study of Huntington's disease caudate nucleus expression profiles and in a meta-analysis of breast cancer studies. In both cases the results were statistically significant and appeared to home in on compact pathways enriched with hallmarks of the diseases.