Recognition of genetic mutations in text using deep learning
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/25972 |
Resumo: | Deep learning is a sub-area of automatic learning that attempts to model complex structures in the data through the application of different neural network architectures with multiple layers of processing. These methods have been successfully applied in areas ranging from image recognition and classification, natural language processing, and bioinformatics. In this work we intend to create methods for named-entity recognition (NER) in text using techniques of deep learning in order to identify genetic mutations. |
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Recognition of genetic mutations in text using deep learningDeep LearningNERBI-LSTM-CRFNeural NetworksArtificial IntelligenceDeep learning is a sub-area of automatic learning that attempts to model complex structures in the data through the application of different neural network architectures with multiple layers of processing. These methods have been successfully applied in areas ranging from image recognition and classification, natural language processing, and bioinformatics. In this work we intend to create methods for named-entity recognition (NER) in text using techniques of deep learning in order to identify genetic mutations.Deep Learning é uma subárea de aprendizagem automática que tenta modelar estruturas complexas no dados através da aplicação de diferentes arquitecturas de redes neuronais com várias camadas de processamento. Estes métodos foram aplicados com sucesso em áreas que vão desde o reconhecimento de imagem e classificação, processamento de linguagem natural e bioinformática. Neste trabalho pretendemos criar métodos para reconhecimento de entidades nomeadas (NER) no texto usando técnicas de Deep Learning, a fim de identificar mutações genéticas.2019-05-08T14:16:19Z2018-01-01T00:00:00Z2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/25972TID:202234045engMatos, Pedro Ferreira deinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:50:21Zoai:ria.ua.pt:10773/25972Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:59:06.281547Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Recognition of genetic mutations in text using deep learning |
title |
Recognition of genetic mutations in text using deep learning |
spellingShingle |
Recognition of genetic mutations in text using deep learning Matos, Pedro Ferreira de Deep Learning NER BI-LSTM-CRF Neural Networks Artificial Intelligence |
title_short |
Recognition of genetic mutations in text using deep learning |
title_full |
Recognition of genetic mutations in text using deep learning |
title_fullStr |
Recognition of genetic mutations in text using deep learning |
title_full_unstemmed |
Recognition of genetic mutations in text using deep learning |
title_sort |
Recognition of genetic mutations in text using deep learning |
author |
Matos, Pedro Ferreira de |
author_facet |
Matos, Pedro Ferreira de |
author_role |
author |
dc.contributor.author.fl_str_mv |
Matos, Pedro Ferreira de |
dc.subject.por.fl_str_mv |
Deep Learning NER BI-LSTM-CRF Neural Networks Artificial Intelligence |
topic |
Deep Learning NER BI-LSTM-CRF Neural Networks Artificial Intelligence |
description |
Deep learning is a sub-area of automatic learning that attempts to model complex structures in the data through the application of different neural network architectures with multiple layers of processing. These methods have been successfully applied in areas ranging from image recognition and classification, natural language processing, and bioinformatics. In this work we intend to create methods for named-entity recognition (NER) in text using techniques of deep learning in order to identify genetic mutations. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01T00:00:00Z 2018 2019-05-08T14:16:19Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/25972 TID:202234045 |
url |
http://hdl.handle.net/10773/25972 |
identifier_str_mv |
TID:202234045 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137644795396096 |