Literature DB >> 12006977

Association of genes to genetically inherited diseases using data mining.

Carolina Perez-Iratxeta1, Peer Bork, Miguel A Andrade.   

Abstract

Although approximately one-quarter of the roughly 4,000 genetically inherited diseases currently recorded in respective databases (LocusLink, OMIM) are already linked to a region of the human genome, about 450 have no known associated gene. Finding disease-related genes requires laborious examination of hundreds of possible candidate genes (sometimes, these are not even annotated; see, for example, refs 3,4). The public availability of the human genome draft sequence has fostered new strategies to map molecular functional features of gene products to complex phenotypic descriptions, such as those of genetically inherited diseases. Owing to recent progress in the systematic annotation of genes using controlled vocabularies, we have developed a scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene. In a benchmark of the system with 100 known disease-associated genes, the disease-associated gene was among the 8 best-scoring genes with a 25% chance, and among the best 30 genes with a 50% chance, showing that there is a relationship between the score of a gene and its likelihood of being associated with a particular disease. The scoring also indicates that for some diseases, the chance of identifying the underlying gene is higher.

Entities:  

Mesh:

Substances:

Year:  2002        PMID: 12006977     DOI: 10.1038/ng895

Source DB:  PubMed          Journal:  Nat Genet        ISSN: 1061-4036            Impact factor:   38.330


  123 in total

1.  OntoBlast function: From sequence similarities directly to potential functional annotations by ontology terms.

Authors:  Günther Zehetner
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

2.  Inferring higher functional information for RIKEN mouse full-length cDNA clones with FACTS.

Authors:  Takeshi Nagashima; Diego G Silva; Nikolai Petrovsky; Luis A Socha; Harukazu Suzuki; Rintaro Saito; Takeya Kasukawa; Igor V Kurochkin; Akihiko Konagaya; Christian Schönbach
Journal:  Genome Res       Date:  2003-06       Impact factor: 9.043

3.  Dragon TF Association Miner: a system for exploring transcription factor associations through text-mining.

Authors:  Hong Pan; Li Zuo; Vidhu Choudhary; Zhuo Zhang; Shoi Houi Leow; Fui Teen Chong; Yingliang Huang; Victor Wui Siong Ong; Bijayalaxmi Mohanty; Sin Lam Tan; S P T Krishnan; Vladimir B Bajic
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

4.  Novel analytical methods applied to type 1 diabetes genome-scan data.

Authors:  Flemming Pociot; Allan E Karlsen; Claus B Pedersen; Mogens Aalund; Jørn Nerup
Journal:  Am J Hum Genet       Date:  2004-03-11       Impact factor: 11.025

5.  Phenotypic information in genomic variant databases enhances clinical care and research: the International Standards for Cytogenomic Arrays Consortium experience.

Authors:  Erin Rooney Riggs; Laird Jackson; David T Miller; Steven Van Vooren
Journal:  Hum Mutat       Date:  2012-03-20       Impact factor: 4.878

Review 6.  Computational tools for prioritizing candidate genes: boosting disease gene discovery.

Authors:  Yves Moreau; Léon-Charles Tranchevent
Journal:  Nat Rev Genet       Date:  2012-07-03       Impact factor: 53.242

7.  A literature search tool for intelligent extraction of disease-associated genes.

Authors:  Jae-Yoon Jung; Todd F DeLuca; Tristan H Nelson; Dennis P Wall
Journal:  J Am Med Inform Assoc       Date:  2013-09-02       Impact factor: 4.497

8.  Beegle: from literature mining to disease-gene discovery.

Authors:  Sarah ElShal; Léon-Charles Tranchevent; Alejandro Sifrim; Amin Ardeshirdavani; Jesse Davis; Yves Moreau
Journal:  Nucleic Acids Res       Date:  2015-09-17       Impact factor: 16.971

9.  The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease.

Authors:  Peter N Robinson; Sebastian Köhler; Sebastian Bauer; Dominik Seelow; Denise Horn; Stefan Mundlos
Journal:  Am J Hum Genet       Date:  2008-10-23       Impact factor: 11.025

10.  Network-based Identification of novel cancer genes.

Authors:  Gabriel Ostlund; Mats Lindskog; Erik L L Sonnhammer
Journal:  Mol Cell Proteomics       Date:  2009-12-03       Impact factor: 5.911

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.