Recognition of genetic mutations in text using deep learning

Detalhes bibliográficos
Autor(a) principal: Matos, Pedro Ferreira de
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.
id RCAP_0f26cfe1b2adb758c702d4059df0345e
oai_identifier_str oai:ria.ua.pt:10773/25972
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling 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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str 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
_version_ 1799137644795396096

Registros relacionados