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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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Autores principales: Pourreza Shahri, Morteza, Kahanda, Indika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520253/
https://www.ncbi.nlm.nih.gov/pubmed/34656098
http://dx.doi.org/10.1186/s12859-021-04421-z
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author Pourreza Shahri, Morteza
Kahanda, Indika
author_facet Pourreza Shahri, Morteza
Kahanda, Indika
author_sort Pourreza Shahri, Morteza
collection PubMed
description 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, automated tools capable of accurately extracting these associations from the biomedical text are in high demand. However, while the manual annotation of protein-phenotype co-mentions required for training such models is highly resource-consuming, extracting millions of unlabeled co-mentions is straightforward. RESULTS: In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions with the help of unlabeled data. This framework allows the ability to incorporate an extensive collection of unlabeled sentence-level co-mentions of human proteins and phenotypes with a small labeled dataset to enhance overall performance. We develop PPPredSS, a prototype of our proposed semi-supervised framework that combines sophisticated language models, convolutional networks, and recurrent networks. Our experimental results demonstrate that the proposed approach provides a new state-of-the-art performance in classifying human protein-phenotype co-mentions by outperforming other supervised and semi-supervised counterparts. Furthermore, we highlight the utility of PPPredSS in powering a curation assistant system through case studies involving a group of biologists. CONCLUSIONS: This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning. The insights and findings from this work have implications for biomedical researchers, biocurators, and the text mining community working on biomedical relationship extraction.
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spelling pubmed-85202532021-10-20 Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes Pourreza Shahri, Morteza Kahanda, Indika BMC Bioinformatics Methodology Article 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, automated tools capable of accurately extracting these associations from the biomedical text are in high demand. However, while the manual annotation of protein-phenotype co-mentions required for training such models is highly resource-consuming, extracting millions of unlabeled co-mentions is straightforward. RESULTS: In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions with the help of unlabeled data. This framework allows the ability to incorporate an extensive collection of unlabeled sentence-level co-mentions of human proteins and phenotypes with a small labeled dataset to enhance overall performance. We develop PPPredSS, a prototype of our proposed semi-supervised framework that combines sophisticated language models, convolutional networks, and recurrent networks. Our experimental results demonstrate that the proposed approach provides a new state-of-the-art performance in classifying human protein-phenotype co-mentions by outperforming other supervised and semi-supervised counterparts. Furthermore, we highlight the utility of PPPredSS in powering a curation assistant system through case studies involving a group of biologists. CONCLUSIONS: This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning. The insights and findings from this work have implications for biomedical researchers, biocurators, and the text mining community working on biomedical relationship extraction. BioMed Central 2021-10-16 /pmc/articles/PMC8520253/ /pubmed/34656098 http://dx.doi.org/10.1186/s12859-021-04421-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology Article
Pourreza Shahri, Morteza
Kahanda, Indika
Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
title Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
title_full Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
title_fullStr Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
title_full_unstemmed Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
title_short Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
title_sort deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520253/
https://www.ncbi.nlm.nih.gov/pubmed/34656098
http://dx.doi.org/10.1186/s12859-021-04421-z
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