2016
DOI: 10.1038/srep34841
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Abstract: A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quanti… Show more

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Cited by 33 publications
(42 citation statements)
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“…The genomescale network containing direct and indirect interactions between gene products was defined using NCBI interaction data 3 . For each gene g i , we used the steady state values (x i , y i , z i ) found by means of the network propagation algorithm described in Bersanelli et al 4 for quantifying the network proximity of g i to altered genes of the three lists. In each list, we selected a number of top ranking genes with the highest steady state values equal to the number of genes occurring in the initial lists.…”
Section: Methodsmentioning
confidence: 99%
“…The genomescale network containing direct and indirect interactions between gene products was defined using NCBI interaction data 3 . For each gene g i , we used the steady state values (x i , y i , z i ) found by means of the network propagation algorithm described in Bersanelli et al 4 for quantifying the network proximity of g i to altered genes of the three lists. In each list, we selected a number of top ranking genes with the highest steady state values equal to the number of genes occurring in the initial lists.…”
Section: Methodsmentioning
confidence: 99%
“…The genomescale network containing direct and indirect interactions between gene products was defined using NCBI interaction data 3 . For each gene g i , we used the steady state values (x i , y i , z i ) found by means of the network propagation algorithm described in Bersanelli et al 4 for quantifying the network proximity of g i to altered genes of the three lists. In each list, we selected a number of top ranking genes with the highest steady state values equal to the number of genes occurring in the initial lists.…”
Section: Methodsmentioning
confidence: 99%
“…Among them DawnRank [20], the algorithm by Shi et al [21], and Subdyquency [23] employ, on top of the overall DriverNet model, versions of heat diffusion on the networks integrating data in the form of biological interactions, mutations, and gene expression. Heat diffusion is a technique employed commonly in many cancer driver gene or gene module discovery algorithms [9,24,25,26,27,28]. It generally serves two purposes simultaneously.…”
Section: Introductionmentioning
confidence: 99%