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SIENA: Semi-automatic semantic enhancement of datasets using concept recognition
BACKGROUND: The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. RESULTS: Thi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992819/ https://www.ncbi.nlm.nih.gov/pubmed/33761996 http://dx.doi.org/10.1186/s13326-021-00239-z |
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author | Grigoriu, Andreea Zaveri, Amrapali Weiss, Gerhard Dumontier, Michel |
author_facet | Grigoriu, Andreea Zaveri, Amrapali Weiss, Gerhard Dumontier, Michel |
author_sort | Grigoriu, Andreea |
collection | PubMed |
description | BACKGROUND: The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. RESULTS: This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results. CONCLUSIONS: Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions. |
format | Online Article Text |
id | pubmed-7992819 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79928192021-03-25 SIENA: Semi-automatic semantic enhancement of datasets using concept recognition Grigoriu, Andreea Zaveri, Amrapali Weiss, Gerhard Dumontier, Michel J Biomed Semantics Research BACKGROUND: The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. RESULTS: This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results. CONCLUSIONS: Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions. BioMed Central 2021-03-24 /pmc/articles/PMC7992819/ /pubmed/33761996 http://dx.doi.org/10.1186/s13326-021-00239-z Text en © The Author(s) 2021 Open Access This 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/. The Creative Commons Public Domain Dedication waiver (http://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 | Research Grigoriu, Andreea Zaveri, Amrapali Weiss, Gerhard Dumontier, Michel SIENA: Semi-automatic semantic enhancement of datasets using concept recognition |
title | SIENA: Semi-automatic semantic enhancement of datasets using concept recognition |
title_full | SIENA: Semi-automatic semantic enhancement of datasets using concept recognition |
title_fullStr | SIENA: Semi-automatic semantic enhancement of datasets using concept recognition |
title_full_unstemmed | SIENA: Semi-automatic semantic enhancement of datasets using concept recognition |
title_short | SIENA: Semi-automatic semantic enhancement of datasets using concept recognition |
title_sort | siena: semi-automatic semantic enhancement of datasets using concept recognition |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992819/ https://www.ncbi.nlm.nih.gov/pubmed/33761996 http://dx.doi.org/10.1186/s13326-021-00239-z |
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