Association of genes to genetically inherited diseases using data mining

By C. Perez-Iratxeta, Peer Bork, Miguel Andrade. Nature Genetics, 2002

384 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4bfbdbcb1c170fc8152c059bc3ccd9cfb02b1469

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Citations in the corpus

Walking the interactome for prioritization of candidate disease genes. (2008, background): “Methods based on functional annotation 5–12 have no restrictions but will presumably function poorly for novel disease genes for which little or no functional annotations are available.”
The Gene Ontology (GO) project in 2006 (2005, methodology): “Biologists have used GO for tasks, such as gene function prediction (17), collaborative construction and analysis of cellular pathways (18), and association of genes to genetically inherited diseases (19).”
Comparative experiments on learning information extractors for proteins and their interactions (2005, background): “Some projects on extracting information from biomedical literature have also employed such learning techniques [42,15,11,46,18,32,47,40].”
Network-based global inference of human disease genes (2008, methodology): “…in which one or more susceptible linkage interval(s) rather than speciﬁc genes have been identiﬁed by linkage analysis, a benchmark test on artiﬁcial linkage intervals around the known disease genes is generally adopted (Perez-Iratxeta et al , 2002; Franke et al , 2006; Lage et al , 2007).”
Mining breast cancer genes with a network based noise-tolerant approach (2013, positive): “On the other hand, several methods rely on functional similarities between candidate and disease genes [15], for example, some methods measured similarity between genes by their functional annotations [16] (e.g., Gene Ontology (GO) [17]).”
Mining knowledge from text using information extraction (2005, background): “Consequently, a growing number of recent projects have focused on developing IE systems for biomedical literature [33; 23; 7; 55; 32].”
ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples (2011, background): “, by matching the functional annotations of candidate genes to the disease or phenotype under investigation [2-4], many successful approaches assume that some disease genes are already known and try to detect candidate genes which share similarities with known disease genes for the phenotype under i”
Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions (2009, background): “In fact, it is even able to identify relatedness between genes that have no established common pathways or article co-citations! In contrast, other strategies start with static gene relationships—such as (1) preconstructed molecular networks [12,16] or sets of gene with common function [11,15] or (2”