Integration of text- and data-mining using ontologies successfully selects disease gene candidates - Wikidata - awgldk - TriplyDB

Integration of text- and data-mining using ontologies successfully selects disease gene candidates

Integration of text- and data-mining using ontologies successfully selects disease gene candidates

http://www.wikidata.org/entity/Q24803121

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Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology Semantics in support of biodiversity knowledge discovery: an introduction to the biological collections ontology and related ontologies.Integrating human omics data to prioritize candidate genes Mining the pharmacogenomics literature--a survey of the state of the art ENDEAVOUR update: a web resource for gene prioritization in multiple species Gene-disease relationship discovery based on model-driven data integration and database view definition Differential repression of alternative transcripts: a screen for miRNA targets.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.MeInfoText: associated gene methylation and cancer information from text mining.Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies.In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles.Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder.Linking genes to diseases: it's all in the data.Gene prioritization and clustering by multi-view text mining Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes.Prioritization and evaluation of depression candidate genes by combining multidimensional data resources.BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs.PhenoGO: assigning phenotypic context to gene ontology annotations with natural language processing.ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples.Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR Improving disease gene prioritization using the semantic similarity of Gene Ontology terms Systematic genotype-phenotype analysis of autism susceptibility loci implicates additional symptoms to co-occur with autism Text mining in cancer gene and pathway prioritization.Associating disease-related genetic variants in intergenic regions to the genes they impact A vertex similarity-based framework to discover and rank orphan disease-related genes.GLAD4U: deriving and prioritizing gene lists from PubMed literature Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.ToppGene Suite for gene list enrichment analysis and candidate gene prioritization A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis.Huntingtin-interacting protein 14 is a type 1 diabetes candidate protein regulating insulin secretion and beta-cell apoptosis Identification of highly related references about gene-disease association.Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations.Update of the G2D tool for prioritization of gene candidates to inherited diseases.Candidate gene identification approach: progress and challenges Bioinformatics methods for identifying candidate disease genes.

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Integration of text- and data-mining using ontologies successfully selects disease gene candidates

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The allelic architecture of human disease genes: common disease-common variant... or not?

Searching for genetic determinants in the new millennium

Mining the bibliome: searching for a needle in a haystack? New computing tools are needed to effectively scan the growing amount of scientific literature for useful information

Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics.

New methods for finding disease-susceptibility genes: impact and potential

Textpresso: an ontology-based information retrieval and extraction system for biological literature

POCUS: mining genomic sequence annotation to predict disease genes

Tough mining: the challenges of searching the scientific literature

INFORMATION ACCESS: Building A "GenBank" of the Published Literature

The Unified Medical Language System (UMLS): integrating biomedical terminology.

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