Search Results

Now showing 1 - 1 of 1

Biyomedikal Bilgi Tabanları Üzerinde Hesaplamalı İlaç Yeniden Konumlandırması İçin Bilgi Grafiği Temsil Yaklaşımları
(2026) Erkantarcı, Betül; Bakal, Mehmet Gökhan; Köse, Abdulkadir
Drug repositioning, or the strategy of finding new medical applications to an existing drug, is a safer and cheaper alternative to development of a new drug. The thesis constructs a unified biomedical knowledge graph by integrating biomedical information resources such as SemMedDB, repoDB, and UMLS, and presents a comprehensive evaluation of seven knowledge graph embedding models: TransE, TransH, TransR, TransD, TransF, ProjE, and RESCAL. To have a biological relevance, a semantic validation pipeline was created by refining the PubMed-based biomedical language model to an accuracy of around 96% in order to determine the plausibility of the suggested drug-disease associations. TransF had the best quantitative performance with a macro area under the precision-recall curve of 0.767, whereas ProjE produced the largest number of semantically plausible hypotheses with 14 literature-supported drug-disease pairs. Moreover, an automated update system, which leverages internet of things, is used to retrieve fresh PubMed evidence on a daily basis to update semantic plausibility scores. Altogether, the knowledge graph embeddings and semantic validation workflow showed numerous new and literature-based drug-disease relations and indicated its potential to become a strong, explainable and data-driven model in computational drug repositioning. Keywords: Computational Drug Repositioning, Knowledge Graph Embedding, Biomedical Knowledge Graph, Semantic Validation, Internet of Things

Yüksek Lisans Tezleri

Browse

Filters

Settings

Sort By

Results per page

Search Results