Cargando…
Literature classification for semi-automated updating of biological knowledgebases
BACKGROUND: As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common typ...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3852072/ https://www.ncbi.nlm.nih.gov/pubmed/24564403 http://dx.doi.org/10.1186/1471-2164-14-S5-S14 |
_version_ | 1782478602747510784 |
---|---|
author | Olsen, Lars Rønn Johan Kudahl, Ulrich Winther, Ole Brusic, Vladimir |
author_facet | Olsen, Lars Rønn Johan Kudahl, Ulrich Winther, Ole Brusic, Vladimir |
author_sort | Olsen, Lars Rønn |
collection | PubMed |
description | BACKGROUND: As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. RESULTS: We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. CONCLUSION: We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases. |
format | Online Article Text |
id | pubmed-3852072 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-38520722013-12-20 Literature classification for semi-automated updating of biological knowledgebases Olsen, Lars Rønn Johan Kudahl, Ulrich Winther, Ole Brusic, Vladimir BMC Genomics Research BACKGROUND: As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. RESULTS: We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. CONCLUSION: We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases. BioMed Central 2013-10-16 /pmc/articles/PMC3852072/ /pubmed/24564403 http://dx.doi.org/10.1186/1471-2164-14-S5-S14 Text en Copyright © 2013 Olsen et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Olsen, Lars Rønn Johan Kudahl, Ulrich Winther, Ole Brusic, Vladimir Literature classification for semi-automated updating of biological knowledgebases |
title | Literature classification for semi-automated updating of biological knowledgebases |
title_full | Literature classification for semi-automated updating of biological knowledgebases |
title_fullStr | Literature classification for semi-automated updating of biological knowledgebases |
title_full_unstemmed | Literature classification for semi-automated updating of biological knowledgebases |
title_short | Literature classification for semi-automated updating of biological knowledgebases |
title_sort | literature classification for semi-automated updating of biological knowledgebases |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3852072/ https://www.ncbi.nlm.nih.gov/pubmed/24564403 http://dx.doi.org/10.1186/1471-2164-14-S5-S14 |
work_keys_str_mv | AT olsenlarsrønn literatureclassificationforsemiautomatedupdatingofbiologicalknowledgebases AT johankudahlulrich literatureclassificationforsemiautomatedupdatingofbiologicalknowledgebases AT wintherole literatureclassificationforsemiautomatedupdatingofbiologicalknowledgebases AT brusicvladimir literatureclassificationforsemiautomatedupdatingofbiologicalknowledgebases |