Prediction of drug-to-drug interactions through zero-shot learning

Αστράς, Νικόλαος; Astras, Nikolaos

dc.contributor.author	Αστράς, Νικόλαος	el
dc.contributor.author	Astras, Nikolaos	en
dc.date.accessioned	2024-05-27T10:32:43Z
dc.date.available	2024-05-27T10:32:43Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/59494
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.27190
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc/3.0/gr/	*
dc.subject	Word Embeddings	en
dc.subject	Drug-to-drug interactions	en
dc.subject	Machine Learning	en
dc.subject	Μάθηση χωρίς δεδομένα	el
dc.subject	Ταξινόμηση πολλαπλών ετικετών	el
dc.subject	Αλληλεπιδράσεις Φαρμάκων	el
dc.subject	Μηχανική Μάθηση	el
dc.subject	Multi-label classification	en
dc.subject	Zero-Shot Learning	en
dc.subject	Διανυσματικές αναπαραστάσεις λέξεων	el
dc.title	Prediction of drug-to-drug interactions through zero-shot learning	en
heal.type	bachelorThesis
heal.classification	Computer Science	en
heal.classification	Πληροφορική	el
heal.language	el
heal.language	en
heal.access	campus
heal.recordProvider	ntua	el
heal.publicationDate	2024-02-01
heal.abstract	Drug-to-drug interactions (DDIs) are a crucial aspect of medication management. While estimates vary, some studies suggest that DDIs may be responsible for up to 20\% of the adverse drug reactions requiring hospitalization. Conventional methods for predicting those interactions rely on analyzing the pharmaceutical properties of drugs, clinical findings, and literature references. In recent years, approaches based on machine learning have emerged as a promising alternative, taking advantage of the vast biomedical data currently available, to identify relations between drugs and side effects, leading to highly accurate predictions. In this thesis, we differentiate by adopting the Zero-shot learning (ZSL) paradigm to tackle the challenge of DDI prediction. ZSL is a modern ML technique, that enables models to generalize beyond the classes encountered during training, and make predictions for unseen classes. To achieve this, we leveraged a ZSL framework that relies on feature vectors extracted from both instances and classes. The framework effectively tries to capture and simplify the complex underlying relationships between different drug pairs and side effects. We should mention that a single drug combination can result in multiple side effects, necessitating appropriate modifications to account for this possibility. Our goal is to develop a DDI prediction pipeline that, with the necessary adjustments, can serve as a valuable resource for identifying and mitigating potential drug-drug interactions.	en
heal.advisorName	Τσανάκας, Παναγιώτης	el
heal.committeeMemberName	Σταφυλοπάτης, Ανδρέας	el
heal.committeeMemberName	Ματσόπουλος, Γεώργιος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	65 σ.	el
heal.fullTextAvailability	false