Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/111317
Author(s): Diogo Vaz Nunes
Title: Predicting drug effectiveness in Cancer Cell Lines using Machine Learning and Graph Mining
Issue Date: 2018-02-21
Abstract: 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 arecharacterizations 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.
Description: O cancro é uma doença heterogênea, com um nivel de diversidade entre tumores considerável. Os biomarcadores, no contexto de uma doença oncológica, permitem a identificação da capacidade de resposta de um paciente a um dado fármaco. Estes tratamentos especificos têm produzido resultados em média superiores aos de uso mais abrangente. No entanto a ligação entre a resposta ao tratamento e o valor de um dado biomarcador é em muitos casos ainda desconhecida. O objectivo deste projecto é, com base em resultados prévios e na caracterização tanto dos fármacos como dos tecidos celulares, conseguir prever a eficácia de um fármaco em um tumor .
Subject: Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
Scientific areas: Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
TID identifier: 201904691
URI: https://repositorio-aberto.up.pt/handle/10216/111317
Document Type: Dissertação
Rights: openAccess
Appears in Collections:FEUP - Dissertação

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