Publicação
Boosting graph neural networks with knowledge for personalized medicine
| datacite.subject.fos | Departamento de Informática | pt_PT |
| dc.contributor.advisor | Pesquita, Cátia, 1980- | |
| dc.contributor.author | Bernardino, Beatriz de Campos | |
| dc.date.accessioned | 2025-02-12T14:33:59Z | |
| dc.date.available | 2025-02-12T14:33:59Z | |
| dc.date.issued | 2025 | |
| dc.date.submitted | 2024 | |
| dc.description | Tese de mestrado, Engenharia Informática, 2025, Universidade de Lisboa, Faculdade de Ciências | pt_PT |
| dc.description.abstract | Cancer is one of the leading causes of death around the globe. Personalized medicine research has been increasing in popularity in recent years due to technological advances such as Machine Learning. However, clinical datasets often have a high number of features, requiring complex processing for ML such as feature extraction, and have limited size which is a problem with Deep Learning methods particularly. This work aims to explore the impact of knowledge graphs’ semantics combined with the learning capabilities of graph neural networks to predict drug response. Specifically, how does using semantics and considering graph data with context in patient drug response association prediction and if knowledge-injected GNNs perform better than knowledge graph embedding approaches To do this, two different approaches were developed: one using knowledge graph embeddings to represent patients’ genetic information and their drug treatment, and another using a dual graph neural network approach that works over the gene and drug knowledge graphs to learn patient and drug representations for supervised learning. The findings of this study were that the semantic context provided by knowledge graphs and ontologies had a positive impact on patient-drug response prediction results. However, the GNNbased approach didn’t achieve better results than the knowledge graph embedding-based results, despite only a slight difference in performance between them. | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/98358 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Previsão da resposta paciente-fármaco | pt_PT |
| dc.subject | aprendizagem automática | pt_PT |
| dc.subject | grafos de conhecimento | pt_PT |
| dc.subject | redes neuronais | pt_PT |
| dc.subject | Teses de mestrado - 2025 | pt_PT |
| dc.title | Boosting graph neural networks with knowledge for personalized medicine | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Tese de mestrado em Engenharia Informática | pt_PT |