Graph Convolutional Neural Networks applied to classify cancer types

Entity: Universitat Rovira i Virgili (URV)

Confidenciality: No

Education area(s): Enginyeria de la Seguretat Informàtica i Intel·ligència Artificial

Title in different languages: Graph Convolutional Neural Networks applied to classify cancer types

Abstract: The main objective of this master's thesis was to reproduce the results published in the paper “Classification of Cancer Types Using Graph Convolutional Neural Networks” written by Ricardo Ramirez et al., since the use of these networks is a relatively new concept. The paper’s goal is to classify tumour tissues correctly using their RNA, through the use of convolutional graph networks (aka. GCNN). Finally, the code supplied with the paper isn’t usable, because there’re some missing parts and is focused in show the results exposed in the paper. For these reasons, many modifications and proposals are shown in this thesis.

Subject: Enginyeria informàtica

Academic year: 2020-2021

Language: en

Work's public defense date: 2021-09-21

Subject areas: Computer engineering

Student: Pascual Saldaña, Heribert

Department: Enginyeria Informàtica i Matemàtiques

Creation date in repository: 2022-05-17

Keywords: GCNN, Cancer, Classification

Title in original language: Graph Convolutional Neural Networks applied to classify cancer types

Access Rights: info:eu-repo/semantics/openAccess

Project director: Julià Ferré, Carme