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• W2080205181 startingPage "CIN.S17641" @default.
• W2080205181 abstract "Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes." @default.
• W2080205181 created "2016-06-24" @default.
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• W2080205181 date "2014-01-01" @default.
• W2080205181 modified "2023-10-14" @default.
• W2080205181 title "Assessment of Subnetwork Detection Methods for Breast Cancer" @default.
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• W2080205181 doi "https://doi.org/10.4137/cin.s17641" @default.
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