Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes

Background: Identifying human protein-phenotype relationships has attracted researchers in bioinformatics and biomedical natural language processing due to its importance in uncovering rare and complex diseases. Since experimental validation of protein-phenotype associations is prohibitive, automate...

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Bibliographic Details
Main Authors:	Kahanda, I. (Author), Pourreza Shahri, M. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	Biomedical relationship extraction data mining Data Mining Deep learning Deep neural networks Ensemble learning Extraction human Human phenotype ontology Human proteins Humans Learning algorithms Natural language processing systems Neural Networks, Computer Ontology's phenotype Phenotype Protein phenotype relationship Protein phenotype relationships Proteins Recurrent neural networks Relationship extraction Semi-supervised Semi-supervised learning supervised machine learning Supervised Machine Learning
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Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes

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