ArticleBioinformatics (Oxford, England)2018
A gene-phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Crop-GPA: an integrated platform of crop gene-phenotype associations.NPJ systems biology and applications · 2024Article
- Text mining for contexts and relationships in cancer genomics literature.Bioinformatics (Oxford, England) · 2024Article
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- Predicting cross-tissue hormone-gene relations using balanced word embeddings.Bioinformatics (Oxford, England) · 2022Article
- AnthraxKP: a knowledge graph-based, Anthrax Knowledge Portal mined from biomedical literature.Database : the journal of biological databases and curation · 2022Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Mining hidden knowledge: embedding models of cause-effect relationships curated from the biomedical literature.Bioinformatics advances · 2022Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.Scientific reports · 2021Article
- A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model.Communications biology · 2020Article
- AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature.Science translational medicine · 2020Article
- Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts.Bioinformatics (Oxford, England) · 2020Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network.BMC medical genomics · 2019Article
- Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer's disease.BMC medical informatics and decision making · 2019Article
- Annotating and detecting phenotypic information for chronic obstructive pulmonary disease.JAMIA open · 2019Article
- Ontology based text mining of gene-phenotype associations: application to candidate gene prediction.Database : the journal of biological databases and curation · 2019Article
- Automated Methods Enable Direct Computation on Phenotypic Descriptions for Novel Candidate Gene Prediction.Frontiers in plant science · 2019Article
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Authors and funding
9 authors.
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Abstract
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.
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