Browsing by Subject "graph neural networks"
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Assessing the applicability of graph neural networks for cancer staging using sample similarity networks
(2022) [Work completion of graduation]Cancer staging is a challenging classification task in which, given the samples’ charac teristics, the employed strategy needs to categorize them into typically one out of four stages. As more public biological data becomes ... -
Graph Neural Networks for image classification : comparing approaches for building graphs
(2024) [Work completion of graduation]Graph Neural Networks (GNNs) is an approach that allows applying deep learning tech niques to non-Euclidean data, such as graphs and manifolds. Over the past few years, graph convolutional networks (GCNs) and graph attention ... -
Learning centrality measures with graph neural networks
(2019) [Dissertation]Centrality Measures are important metrics used in Social Network Analysis. Such measures allow one to infer which entity in a network is more central (informally, more important) than another. Analyses based on centrality ... -
A study on graph neural networks for classification tasks and model interpretability on genomic datasets
(2024) [Work completion of graduation]Recently, a few works have started proposing the use of graph neural networks (GNNs) to embed knowledge of gene interactions in machine learning models and thus produce more robust classifiers for genomic classification ...