Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/111317 |
Resumo: | Cancer is an heterogeneous disease, with a high degree of diversity between tumours. Biomarkers, in the context of an oncological disease, allow the identification of the response from a patient to a given drug. These specific treatments have been producing results that are superior on average to broader ones. However, the relationship between a drug's response a biomarkers value is in many cases yet unknown. Some models to predict this relationship have already been built, using machine learning methods. The input are characterizations of both the drug and the tissue along with the result of the drug's use on a given tissue. The goal of this thesis is to improve on previous models and the characterization of both the drug and the tissue through the introduction of graph mining and other machine learning methods. |
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Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph MiningEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringCancer is an heterogeneous disease, with a high degree of diversity between tumours. Biomarkers, in the context of an oncological disease, allow the identification of the response from a patient to a given drug. These specific treatments have been producing results that are superior on average to broader ones. However, the relationship between a drug's response a biomarkers value is in many cases yet unknown. Some models to predict this relationship have already been built, using machine learning methods. The input are characterizations of both the drug and the tissue along with the result of the drug's use on a given tissue. The goal of this thesis is to improve on previous models and the characterization of both the drug and the tissue through the introduction of graph mining and other machine learning methods.2018-02-212018-02-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/111317TID:201904691porDiogo Vaz Nunesinfo: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:RCAAP2023-11-29T13:39:52Zoai:repositorio-aberto.up.pt:10216/111317Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:45:06.411267Repositório 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 |
Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining |
title |
Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining |
spellingShingle |
Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining Diogo Vaz Nunes Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
title_short |
Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining |
title_full |
Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining |
title_fullStr |
Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining |
title_full_unstemmed |
Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining |
title_sort |
Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining |
author |
Diogo Vaz Nunes |
author_facet |
Diogo Vaz Nunes |
author_role |
author |
dc.contributor.author.fl_str_mv |
Diogo Vaz Nunes |
dc.subject.por.fl_str_mv |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
description |
Cancer is an heterogeneous disease, with a high degree of diversity between tumours. Biomarkers, in the context of an oncological disease, allow the identification of the response from a patient to a given drug. These specific treatments have been producing results that are superior on average to broader ones. However, the relationship between a drug's response a biomarkers value is in many cases yet unknown. Some models to predict this relationship have already been built, using machine learning methods. The input are characterizations of both the drug and the tissue along with the result of the drug's use on a given tissue. The goal of this thesis is to improve on previous models and the characterization of both the drug and the tissue through the introduction of graph mining and other machine learning methods. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-02-21 2018-02-21T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/111317 TID:201904691 |
url |
https://hdl.handle.net/10216/111317 |
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TID:201904691 |
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por |
language |
por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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