A new web-based data mining tool for the identification of candidate genes for human genetic disorders

Driel, M.A. Van; Cuelenaere, Koen; Kemmeren, Patrick; Leunissen, Jack A. M.; Brunner, Han G.

doi:10.1038/sj.ejhg.5200918

Cited by 79 publications

(54 citation statements)

References 25 publications

(19 reference statements)

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“…GeneSeeker is a web tool that selects candidate genes of the interest disease based on gene expression and phenotypic data from human and mouse (van Driel et al, 2003). eVOC system performs candidate gene selection based on the co-occurrence of disease name in PubMed abstracts through data-mining methods (Tiffin et al, 2005 Ontology (GO) (Ashburner et al, 2000), InterPro and expression data.…”

Section: Text and Data Mining Methodsmentioning

confidence: 99%

Disease Gene Prioritization

Arias¹,

Yeh²,

Soo³

2011

Selected Works in Bioinformatics

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Section: Text and Data Mining Methodsmentioning

confidence: 99%

Disease Gene Prioritization

Arias¹,

Yeh²,

Soo³

2011

Selected Works in Bioinformatics

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“…The positional cloning and biological candidate gene studies have undoubtedly uncovered new genetically validated drug targets, and there are also some unique computational methods that attempt to score potential diseaserelevant genes based on functional similarities to known disease genes that cause similar phenotypes, and/or use expression data that correlate with the phenotype of the disease for the prediction of diseaserelevant genes [Freudenberg and Propping, 2002;Perez-Iratxeta et al, 2002;Van Driel et al, 2003]. However, not all the potential disease-relevant genes will be chemically or biologically tractable; thus, one more reasonable strategy for identifying therapy targets is to adapt the candidate gene approach and limit its application to a certain target family (e.g., GPCRs, ion channels, or proteases) [Spiegel and Weinstein, 2004].…”

Section: Genetics Analysis Reveals Clues To Gpcrs Identificationmentioning

confidence: 99%

Computational identification and analysis of G protein‐coupled receptor targets

Feng

Jiang

2006

Drug Development Research

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The completion of the human genome sequence has provided a large pool of potential drug targets for disease therapy. G protein–coupled receptors (GPCRs), which are central to signaling networks that regulate basic cellular processes, represent the most important known class of therapeutic targets for multiple disease states. Bioinformatics approaches can be applied to facilitate the identification of novel GPCRs, understanding their physiological and pathological roles, and screening for drug discovery. The present review summarizes current bioinformatics approaches that can be used to identify and analyze GPCR targets. In addition, the limitations of these technologies with the intention of setting reasonable expectations are also discussed together with some potential avenues for GPCR research. Drug Dev. Res. 67:771–780, 2006. © 2007 Wiley‐Liss, Inc.

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“…For example, according to the "guilt-by-direct-association" principle, the prioritization is enabled by ranking candidate genes in a susceptibility region according to their relevance to genes that are already known to be associated with the query disease under investigation (i.e., seed genes). Based on this principle, a wide variety of information, including protein sequences [3,4], gene expression profiles [4][5][6], functional annotations [6][7][8][9], literature descriptions [4,5,10], protein-protein interactions (PPI) [5,6,11,12], and many others [13], has been used to facilitate the prioritization of candidate genes.…”

Section: Introductionmentioning

confidence: 99%

Constructing human phenome-interactome networks for the prioritization of candidate genes

Chen

Zhang

Gan

et al. 2012

Statistics and Its Interface

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Although remarkable success has been achieved by traditional gene-mapping methods in locating genes associated with inherited human diseases, the resulting chromosomal regions are usually large, containing tens or even hundreds of genes. Therefore, it is indispensable to develop computational methods for the identification of genes that are truly responsible for diseases from candidate genes. To tackle this problem, several methods have been proposed to use both a phenotype similarity profile (phenome) and a proteinprotein interaction network (interactome) for the prioritization of candidate genes. The use of the phenome broadens the scope of applications of these methods for identifying disease-associated genes, and the use of the interactome provides a reliable measure of functional similarities between genes. These two data sources, together with carefully designed computational models, result in computational methods with superior performance in the prioritization of candidate genes for a given query disease of interest. In this paper, we review recent achievements of such computational methods that rely on the integration of the phenome and the interactome to prioritize candidate genes. We also summarize how similar methods can be readily used in identifying microRNAs that are potentially involved in complex diseases and discovering drugs that may target on disease-associated proteins. Finally, we discuss future prospects and challenges for the integration of multiple genomic data sources to systematically discover genes that underlie human diseases.

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A new web-based data mining tool for the identification of candidate genes for human genetic disorders

Cited by 79 publications

References 25 publications

Disease Gene Prioritization

Disease Gene Prioritization

Computational identification and analysis of G protein‐coupled receptor targets

Constructing human phenome-interactome networks for the prioritization of candidate genes

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