A Network-Based Method for Predicting Disease-Causing Genes

Karni, Shaul; Soreq, Hermona; Sharan, Roded

doi:10.1089/cmb.2008.05tt

Cited by 85 publications

(59 citation statements)

References 37 publications

(35 reference statements)

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“…Mani et al proposed a method called Interactome Dysregulation Enrichment Analysis (IDEA) to predict cancer related genes using interactome and microarray data (Mani et al, 2008). Karni, Soreq, and Sharan attempted to predict the causal gene from expression profile data and they identified a set of disease-related genes that could best explain the expression changes of the disease-related genes in terms of probable pathways leading from the causal to the affected genes in the network (Karni et al, 2009). Tables 1 and 2 show a summary of the aforementioned methods.…”

Section: Network Based Methodsmentioning

confidence: 99%

Disease Gene Prioritization

Arias¹,

Yeh²,

Soo³

2011

Selected Works in Bioinformatics

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Section: Network Based Methodsmentioning

confidence: 99%

Disease Gene Prioritization

Arias¹,

Yeh²,

Soo³

2011

Selected Works in Bioinformatics

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“…Several techniques uncovers gene-disease associations taking an integrative approach, leveraging Gene Ontology annotations [1][2][3][4][5][6], genes expression [7][8],protein sequences [9], biological pathways [4], Bio-text mining [10][11], and transcription factor binding sites [4] and several phenotypic traits of diseases.…”

Section: Introductionmentioning

confidence: 99%

Protein Network for Associating Genes with Dementia

Gupta¹,

Mishra²

2013

IJCA

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Association between causal genes and their genetic diseases is an important problem concerning human health. Linkage analysis is such a method that can identify which unknown disease genes are located in chromosomal region out of hundreds of candidate genes according to their functions, interactions, and pathways which is good identification of genes associated with general/hereditary disorders.Here, we used method for prioritization of candidate genes of Dementia by the use of a global network distance measure, Random Walk Analysis, which detects neurological disorder been associated with distribution of sub-network among the genes.

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“…In the last few years, many algorithms have been developed to utilize PPI networks in disease gene prioritization (Navlakha and Kingsford, 2010;Franke et al, 2006;Ideker and Sharan, 2008;Karni et al, 2009;Oti et al, 2006;Chen et al, 2009a;Köhler et al, 2008;Vanunu et al 2010;Zhang et al, 2010;Wu et al, 2008;Missiuro et al, 2009;Aerts et al, 2006). These algorithms take as input a set of seed proteins (coded by genes known to be associated with the disease of interest or similar diseases), candidate proteins (coded by genes in the linkage interval for the disease of interest), and a network of interactions among human proteins.…”

Section: Introductionmentioning

confidence: 99%

Vavien: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks

Erten

Bebek

Koyutürk

2011

Journal of Computational Biology

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Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow-based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the ''topological profile'' of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.

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A Network-Based Method for Predicting Disease-Causing Genes

Cited by 85 publications

References 37 publications

Disease Gene Prioritization

Disease Gene Prioritization

Protein Network for Associating Genes with Dementia

Vavien: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks

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