Gene prioritization through genomic data fusion

By S. Aerts, D. Lambrechts, Sunit Maity, P. Loo, Bert Coessens, F. Smet, Léon-Charles Tranchevent, B. De Moor, P. Marynen, Bassem A. Hassan and 2 more. Nature Biotechnology, 2006

947 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f68cafeae5527cd686dac1064feac6b41ad981ce

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Citations in the corpus

Ensembles for feature selection: A review and future trends (2019, neutral): “For example, Stuart et al. [44] introduced the (cid:12)rst attempt to use order statistics in the combination of rankings, although the computational scheme for their method was further optimized by Aerts et al. [45].”
Walking the interactome for prioritization of candidate disease genes. (2008, methodology): “10 In order to compare different curves obtained by ROC analysis, we calculate the area under the ROC curve (AUROC) for each curve.”
Disease candidate gene identification and prioritization using protein interaction networks (2009, background): “Background Most of the current disease candidate gene prioritization methods [1-6] rely on functional annotations.”
Approaches for extracting practical information from gene co-expression networks in plant biology. (2007, neutral): “The next steps include development of methodologies to integrate multiple omics data sets, as has been proposed for human and zebrafish (Aerts et al. 2006, Butte and Kohane 2006).”
Improved exome prioritization of disease genes through cross-species phenotype comparison (2014, methodology): “…Research 343 www.genome.org methods were developed to integrate complex and heterogenous data sets including expression data, genetic sequences, functional annotations, protein–protein interaction networks, and the medi- sion with the previously described Endeavour algorithm (Aerts et al. 2006).”
A bagging SVM to learn from positive and unlabeled examples (2010, background): “In gene ranking, the query is a ﬁnite list of genes known to have a given function or to be associated to a given disease, and the goal is to identify new genes sharing the same property (Aerts et al., 2006).”
WBSMDA: Within and Between Score for MiRNA-Disease Association prediction (2016, methodology): “Furthermore, most of computational methods were developed based on the assumption that functionally similar miRNAs usually have connection with phenotypically similar diseases 40–42 .”
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections (2014, methodology): “The rankings for different species are combined by rank aggregation [35] into one final ranking for each PWM in our library.”
Network-based global inference of human disease genes (2008, methodology): “These computational methods are largely based on the similarity of characteristics of disease genes, including sequence features (Adie et al , 2005; Aerts et al , 2006), expression patterns (van Driel et al , 2003; Aerts et al , 2006; Franke et al , 2006), functional annotations (Freudenberg and…”
Mining breast cancer genes with a network based noise-tolerant approach (2013, methodology): “For instance, ENDEAVOUR [24] is an online tool based on using multiple data sources.”
Methods for the integration of multi-omics data: mathematical aspects (2016, background): “Endeavour [37] General Integration of omics-specific ranks with order statistics Webserver”
ToppGene Suite for gene list enrichment analysis and candidate gene prioritization (2009, methodology): “Except for ENDEAVOUR (5,14) and ToppGene (10), most of the existing approaches mainly focus on the combination of few data sources.”