@article { ISI:000265114100004,
	title = {Discovery and Expansion of Gene Modules by Seeking Isolated Groups in a Random Graph Process},
	journal = {PLOS ONE},
	volume = {3},
	number = {10},
	year = {2008},
	month = {OCT 9},
	pages = {e3358},
	publisher = {PUBLIC LIBRARY SCIENCE},
	type = {Article},
	address = {185 BERRY ST, STE 1300, SAN FRANCISCO, CA 94107 USA},
	abstract = {Background: A central problem in systems biology research is the identification and extension of biological modules groups of genes or proteins participating in a common cellular process or physical complex. As a result, there is a persistent need for practical, principled methods to infer the modular organization of genes from genome-scale data. Results: We introduce a novel approach for the identification of modules based on the persistence of isolated gene groups within an evolving graph process. First, the underlying genomic data is summarized in the form of ranked gene-gene relationships, thereby accommodating studies that quantify the relevant biological relationship directly or indirectly. Then, the observed gene-gene relationship ranks are viewed as the outcome of a random graph process and candidate modules are given by the identifiable subgraphs that arise during this process. An isolation index is computed for each module, which quantifies the statistical significance of its survival time. Conclusions: The Miso (module isolation) method predicts gene modules from genomic data and the associated isolation index provides a module-specific measure of confidence. Improving on existing alternative, such as graph clustering and the global pruning of dendrograms, this index offers two intuitively appealing features: (1) the score is module-specific; and (2) different choices of threshold correlate logically with the resulting performance, i.e. a stringent cutoff yields high quality predictions, but low sensitivity. Through the analysis of yeast phenotype data, the Miso method is shown to outperform existing alternatives, in terms of the specificity and sensitivity of its predictions.},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0003358},
	author = {Brumm, Jochen and Conibear, Elizabeth and Wasserman, Wyeth W. and Bryan, Jennifer}
}
