Results 11 to 20 of about 2,805,831 (274)
Chapter 15: disease gene prioritization. [PDF]
PLoS Computational Biology, 2013 Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming.
Yana Bromberg
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An Outranking Approach for Gene Prioritization Using Multinetworks [PDF]
International Journal of Computational Intelligence Systems, 2021 High-throughput experimental techniques such as genome-wide association studies have been instrumental in the identification of disease-associated genes.
Jesús Jaime Solano Noriega +3 more
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Candidate gene prioritization with Endeavour [PDF]
Nucleic Acids Research, 2016 Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a few are truly relevant to the disease, phenotype, or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and ...
Léon-Charles Tranchevent +6 more
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MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization
BMC Bioinformatics, 2018 Background Prioritizing genes according to their associations with a cancer allows researchers to explore genes in more informed ways. By far, Gene-centric or network-centric gene prioritization methods are predominated.
Lingtao Su +4 more
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An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links. [PDF]
PLoS ONE, 2013 Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes.
Chad Kimmel, Shyam Visweswaran
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Candidate gene prioritization using graph embedding [PDF]
2020 RIVF International Conference on Computing and Communication Technologies (RIVF), 2020 Abstract Candidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task.
Quan Do, Pierre Larmande
core +5 more sources
Disease candidate gene identification and prioritization using protein interaction networks
BMC Bioinformatics, 2009 Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate
Aronow Bruce J, Chen Jing, Jegga Anil G
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Gene Prioritization by Compressive Data Fusion and Chaining. [PDF]
PLoS Computational Biology, 2015 Data integration procedures combine heterogeneous data sets into predictive models, but they are limited to data explicitly related to the target object type, such as genes. Collage is a new data fusion approach to gene prioritization.
Marinka Žitnik +5 more
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A large-scale benchmark of gene prioritization methods [PDF]
Scientific Reports, 2017 AbstractIn order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly analyzed and benchmarked gene prioritization tools. While prospective benchmarks are underpowered to provide statistically significant results in their
Guala D, Sonnhammer ELL
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Disease gene prioritization with quantum walks. [PDF]
BioinformaticsAbstract Motivation Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases.
Saarinen H +4 more
europepmc +4 more sources