2024-03-29T13:55:55Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/6759802022-11-11T14:11:49Zcom_10803_365col_10803_402217
nam a 5i 4500
Medicaments
Medicaments--Direcció cap a l'objectiu
Aprenentatge automàtic
Associació proteïnes-reaccions adverses
Algoritmes computacionals
Base de dades
Exploring the Drug-Adverse Reaction and Drug-Target Landscape through Networks, Statistics and Machine Learning Approaches
[Barcelona] :
Universitat de Vic - Universitat Central de Catalunya,
2022
Accés lliure
http://hdl.handle.net/10803/675980
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Galletti, Cristiano,
autor
Bioinformàtica,
degree
1 recurs en línia (227 pàgines)
Tesi
Doctorat
Universitat de Vic - Universitat Central de Catalunya. Departament de Biociències
2022
Universitat de Vic - Universitat Central de Catalunya. Departament de Biociències
Tesis i dissertacions electròniques
Fernández Fuentes, Narcís,
supervisor acadèmic
TDX
The amount of attrition in drug discovery, particularly at advanced stages, is quite high due to unexpected adverse drug reactions (ADRs) generated by drug candidates; hence, being able to anticipate unexpected reactions when modulating specific protein targets would contribute to the creation of safer drugs and have significant economic ramifications. In this thesis I present three distinct approaches to understand the many facets of this biological problem: the T-ARDIS database, a resource that provides comprehensive information on proteins and associated ADRs; the DocTOR framework, a target-centric approach to predicting associations between protein targets and ADRs by combining information from T-ARDIS drug-protein and drug-ADR databases; and finally, the SONG approach, a network-based viewpoint that considers the protein-ADR relationship in a more interconnected environment, improving our understanding of the protein's functional properties and leading directly to the molecular basis of ADR and ADRs associations.
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