@article {bioinflmu-552,
	title = {{New methods for joint analysis of biological networks and expression data}},
	journal = {Bioinformatics},
	volume = {20},
	number = {10},
	year = {2004},
	pages = {1517-1521},
	abstract = {SUMMARY: Biological networks, such as protein interaction, regulatory

or metabolic networks, derived from public databases, biological experiments or

text mining can be useful for the analysis of high-throughput experimental data.

We present two algorithms embedded in the ToPNet application that show promising

performance in analyzing expression data in the context of such networks. First,

the Significant Area Search algorithm detects subnetworks consisting of

significantly regulated genes. These subnetworks often provide hints on which

biological processes are affected in the measured conditions. Second, Pathway

Queries allow detection of networks including molecules that are not necessarily

significantly regulated, such as transcription factors or signaling proteins.

Moreover, using these queries, the user can formulate biological hypotheses and

check their validity with respect to experimental data. All resulting networks

and pathways can be explored further using the interactive analysis tools

provided by ToPNet program.},
	doi = {10.1093/bioinformatics/bth112},
	pdf = {<a href="/files/oldwebfm//Publications/New_methods_for_joint_analysis_of_biological_networks_and_expression_data.pdf">PDF</a>},
	author = {Florian Sohler and Daniel Hanisch and Ralf Zimmer}
}