integrating gene expression and network, a reference collection

Ideker,T. et al. (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18, S233–S240.

Convert p-value of differential expression into Z-scores based using inverse Gaussian CDF.

Maybe because Ideker02 is looking for 'active subnetwork', only positive Z-score were used. No, both positive and negative Z-score were calculated.
Ideker02 seems to combine K-means and simulated annealing for network clustering. 

Tornow,S. and Mewes,H.W. (2003) Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Res., 31, 6283–6289.

Segal,E. et al. (2003) Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics, 19, 264–272.

Morrison,J.L. et al. (2005) GeneRank: using search engine technology for the analysis of microarray experiments. BMC Bioinformatics, 6, 233.

Ma, X., Lee, H., Wang, L., Sun, F.: ‘CGI: a new approach for prioritizing genes by combining gene expression and protein–protein interaction data’, Bioinformatics, 2007, 23, pp. 215–221

Integrating gene expression and protein-protein interaction network to prioritize cancer-associated

genes, Chao Wu, Jun Zhu  and Xuegong Zhang

http://www.biomedcentral.com/1471-2105/13/182
http://scholar.google.com/scholar?cites=14200881095439672925&as_sdt=5,43&sciodt=0,43&hl=en

Li et al. BMC Medical Genomics 2014, 7(Suppl 2):S4 Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation

http://www.biomedcentral.com/1755-8794/7/S2/S4

http://hongqinlab.blogspot.com/2014/12/li14-bmc-medical-genomics-predict.html

http://hongqinlab.blogspot.com/2014/12/integrating-gene-expression-data-into.html

http://hongqinlab.blogspot.com/2014/03/build-human-gene-network-reliability.html

From Ma, 2007 Bioinformatics CGI paper:

Gene expression data and protein interaction data have been
integrated for gene function prediction. For example, Ideker
et al. (2002) used protein interaction data and gene expression
data to screen for differentially expressed subnetworks between
different conditions. In Tornow and Mewes (2003) and Segal
et al. (2003), gene expression data and protein interactions are
used to group genes into functional modules. These methods provide
insights into the regulatory modules of the whole networks at
the systems biology level. However, it is not clear how to adapt their
methods to identify genes contributing to the phenotype of interest.
Morrison et al. (2005) adapted the Google search engine to prioritize
genes for a phenotype by integrating gene expression profiles
and protein interaction data. However, the algorithm ignores the
information from proteins linked to the target protein through other
intermediate proteins, referred to in the rest of this paper as indirect
neighbors.

Qin: Did the previous methods use human pathogenic genes? Seems not if they did not cite dbSNP or OMIM. 

https://scholar.google.com/scholar?q=disease&btnG=&hl=en&as_sdt=5%2C43&sciodt=0%2C43&cites=5934830469117211620&scipsc=1

X. Zhou, M.-C. J. Kao, and W. H. Wong. Transitive functional annotation by shortest-path analysis of gene expression data. Proc Natl Acad Sci U S A, 99(20):12783–12788, Oct 2002

WGCNA: an R package for weighted correlation network analysis.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559