POCUS: mining genomic sequence annotation to predict disease genes - Test2 - elhamkhani - TriplyDB

POCUS: mining genomic sequence annotation to predict disease genes

POCUS: mining genomic sequence annotation to predict disease genes

http://www.wikidata.org/entity/Q24793367

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In silico gene prioritization by integrating multiple data sources Advances in translational bioinformatics: computational approaches for the hunting of disease genes Integration of text- and data-mining using ontologies successfully selects disease gene candidates Speeding disease gene discovery by sequence based candidate prioritization G2D: a tool for mining genes associated with disease.A web tool for finding gene candidates associated with experimentally induced arthritis in the rat.Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology Integrating human omics data to prioritize candidate genes ENDEAVOUR update: a web resource for gene prioritization in multiple species Prioritizing genes of potential relevance to diseases affected by sex hormones: an example of myasthenia gravis Gene-disease relationship discovery based on model-driven data integration and database view definition Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data.Identification of novel therapeutics for complex diseases from genome-wide association data.Pinpointing disease genes through phenomic and genomic data fusion.Analysis of protein sequence and interaction data for candidate disease gene prediction.Improved human disease candidate gene prioritization using mouse phenotype Computational selection and prioritization of candidate genes for fetal alcohol syndrome.Prediction of human disease genes by human-mouse conserved coexpression analysis.Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies.Identifying hypothetical genetic influences on complex disease phenotypes.Disease candidate gene identification and prioritization using protein interaction networks In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles.Linking genes to diseases: it's all in the data.Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets.Gene prioritization and clustering by multi-view text mining Integration of multiple data sources to prioritize candidate genes using discounted rating system A quantitative approach to study indirect effects among disease proteins in the human protein interaction network.An integrated approach to inferring gene-disease associations in humans.Transactional database transformation and its application in prioritizing human disease genes.Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data.An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.Network-based Identification of novel cancer genes.Integrating multiple protein-protein interaction networks to prioritize disease genes: a Bayesian regression approach Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes.DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases.BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs.ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples.Analysis of genome-wide association study data using the protein knowledge base Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR

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POCUS: mining genomic sequence annotation to predict disease genes

http://www.wikidata.org/entity/Q24793367

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2003 nî lūn-bûn

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2003 թվականին հրատարակված գիտական հոդված

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2003年の論文

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2003年論文

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2003年論文

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POCUS: mining genomic sequence annotation to predict disease genes

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POCUS: mining genomic sequence annotation to predict disease genes

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POCUS: mining genomic sequence annotation to predict disease genes

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POCUS: mining genomic sequence annotation to predict disease genes

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POCUS: mining genomic sequence annotation to predict disease genes

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POCUS: mining genomic sequence annotation to predict disease genes

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Colin A M Semple

http://www.w3.org/2001/XMLSchema#string

Daniel R Clutterbuck

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Frances S Turner

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Initial sequencing and analysis of the human genome

Searching for genetic determinants in the new millennium

Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism

DAVID: Database for Annotation, Visualization, and Integrated Discovery

The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003

The structure and expression of the human neuroligin-3 gene

The InterPro database, 2003 brings increased coverage and new features

Database resources of the National Center for Biotechnology

Association of genes to genetically inherited diseases using data mining.

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

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10.1186/GB-2003-4-11-R75

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