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	title = {MODA: MOdule Differential Analysis for weighted gene co-expression network},
	year = {2016},
	doi = {10.1101/053496},
	publisher = {Cold Spring Harbor Labs Journals},
	abstract = {Gene co-expression network differential analysis is designed to help biologists understand gene expression patterns under different conditions. We have implemented an R package called MODA (Module Differential Analysis) for gene co-expression network differential analysis. Based on transcriptomic data, MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. The usefulness of the method is also demonstrated by synthetic data as well as Daphnia magna gene expression data under different environmental stresses.},
	URL = {http://biorxiv.org/content/early/2016/06/03/053496},
	eprint = {http://biorxiv.org/content/early/2016/06/03/053496.full.pdf},
	journal = {bioRxiv}
}
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  year={2016}
}