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10.1093/bioinformatics/bty263 http://scihub22266oqcxt.onion/10.1093/bioinformatics/bty263 C6022650!6022650 !29950017     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29950017 &cmd=llinks): Failed to open stream: HTTP request failed! 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A gene-phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach |pdf=|usr=}}{{29950017 }} gab.com Text A gene-phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29950017 #FOAvip MOTIVATION: The fundamental challenge of modern genetic analysis is to establish gene-phenotype correlations that are often found in the large-scale publications. Because lexical features of gene are relatively regular in text, the main challenge of these relation extraction is phenotype recognition. Due to phenotypic descriptions are often study- or author-specific, few lexicon can be used to effectively identify the entire phenotypic expressions in text, especially for plants. RESULTS: We have proposed a pipeline for extracting phenotype, gene and their relations from biomedical literature. Combined with abbreviation revision and sentence template extraction, we improved the unsupervised word-embedding-to-sentence-embedding cascaded approach as representation learning to recognize the various broad phenotypic information in literature. In addition, the dictionary- and rule-based method was applied for gene recognition. Finally, we integrated one of famous information extraction system OLLIE to identify gene-phenotype relations. To demonstrate the applicability of the pipeline, we established two types of comparison experiment using model organism Arabidopsis thaliana. In the comparison of state-of-the-art baselines, our approach obtained the best performance (F1-Measure of 66.83%). We also applied the pipeline to 481 full-articles from TAIR gene-phenotype manual relationship dataset to prove the validity. The results showed that our proposed pipeline can cover 70.94% of the original dataset and add 373 new relations to expand it. AVAILABILITY AND IMPLEMENTATION: The source code is available at http://www.wutbiolab.cn: 82/Gene-Phenotype-Relation-Extraction-Pipeline.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Twit Text FOAVip A gene-phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29950017 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29950017 #FOAvip English Wikipedia <ref>{{Cite pmid|29950017 }}</ref> A gene-phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach #MMPMID29950017 Xing W ; Qi J ; Yuan X ; Li L ; Zhang X ; Fu Y ; Xiong S ; Hu L ; Peng J Bioinformatics 2018[Jul]; 34 (13 ): i386-i394 PMID29950017 show ga MOTIVATION: The fundamental challenge of modern genetic analysis is to establish gene-phenotype correlations that are often found in the large-scale publications. Because lexical features of gene are relatively regular in text, the main challenge of these relation extraction is phenotype recognition. Due to phenotypic descriptions are often study- or author-specific, few lexicon can be used to effectively identify the entire phenotypic expressions in text, especially for plants. RESULTS: We have proposed a pipeline for extracting phenotype, gene and their relations from biomedical literature. Combined with abbreviation revision and sentence template extraction, we improved the unsupervised word-embedding-to-sentence-embedding cascaded approach as representation learning to recognize the various broad phenotypic information in literature. In addition, the dictionary- and rule-based method was applied for gene recognition. Finally, we integrated one of famous information extraction system OLLIE to identify gene-phenotype relations. To demonstrate the applicability of the pipeline, we established two types of comparison experiment using model organism Arabidopsis thaliana. In the comparison of state-of-the-art baselines, our approach obtained the best performance (F1-Measure of 66.83%). We also applied the pipeline to 481 full-articles from TAIR gene-phenotype manual relationship dataset to prove the validity. The results showed that our proposed pipeline can cover 70.94% of the original dataset and add 373 new relations to expand it. AVAILABILITY AND IMPLEMENTATION: The source code is available at http://www.wutbiolab.cn: 82/Gene-Phenotype-Relation-Extraction-Pipeline.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |*Software [MESH] |Data Mining/*methods [MESH] |Databases, Bibliographic [MESH] |Genetic Association Studies/*methods [MESH] |Genotype [MESH] |Machine Learning [MESH] |Phenotype [MESH]A gene-phenotype relationship extraction pipeline from the biomedical (epmc).pdf DeepDyve Pubget Overpricing