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MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge

BACKGROUND: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly app...

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Detalles Bibliográficos
Autores principales: Ijaz, Ali Z, Song, Min, Lee, Doheon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3165192/
https://www.ncbi.nlm.nih.gov/pubmed/20406501
http://dx.doi.org/10.1186/1471-2105-11-S2-S3
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author Ijaz, Ali Z
Song, Min
Lee, Doheon
author_facet Ijaz, Ali Z
Song, Min
Lee, Doheon
author_sort Ijaz, Ali Z
collection PubMed
description BACKGROUND: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge. METHODS: We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships. RESULTS: We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses. CONCLUSIONS: Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model.
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spelling pubmed-31651922011-09-03 MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge Ijaz, Ali Z Song, Min Lee, Doheon BMC Bioinformatics Proceedings BACKGROUND: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge. METHODS: We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships. RESULTS: We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses. CONCLUSIONS: Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model. BioMed Central 2010-04-16 /pmc/articles/PMC3165192/ /pubmed/20406501 http://dx.doi.org/10.1186/1471-2105-11-S2-S3 Text en Copyright ©2010 Song and Lee; 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 Proceedings
Ijaz, Ali Z
Song, Min
Lee, Doheon
MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge
title MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge
title_full MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge
title_fullStr MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge
title_full_unstemmed MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge
title_short MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge
title_sort mkem: a multi-level knowledge emergence model for mining undiscovered public knowledge
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3165192/
https://www.ncbi.nlm.nih.gov/pubmed/20406501
http://dx.doi.org/10.1186/1471-2105-11-S2-S3
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