Drug Repositioning via Entity Transformation in Biomedical Knowledge Systems
3rd International Congress of Electrical and Computer Engineering, ICECENG 2024, Bandirma, Türkiye, 27 - 30 Kasım 2024, ss.177-191, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1007/978-3-031-88999-8_14
- Basıldığı Şehir: Bandirma
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.177-191
- Anahtar Kelimeler: Computational drug repositioning, Entity embedding, Medical informatics, Neural networks
- Ankara Üniversitesi Adresli: Hayır
Özet
The drug discovery process for known diseases is crucial in bioinformatics, given the extensive clinical trials, regulatory approvals, and high costs. Computational in silico methods are essential to mitigate these challenges, as they help identify promising drug candidates, thereby reducing the time and cost associated with drug discovery. An effective strategy in this domain is drug repositioning, where existing drugs, already approved for one disease, are repurposed for treating another. This approach is advantageous as it leverages the established safety profiles of existing drugs, avoiding toxic effects on human metabolism. In this effort, we employed a translational entity embedding-based neural network model to advance drug repositioning efforts. We utilize the Semantic Medline Database (SemMedDB) as the primary source of biomedical entity relationships for model training. The model is validated using repoDB, a gold standard dataset for drug repositioning. Technically, the model will learn to minimize the vector distance between related entities. This distance will serve as the basis for predicting potential drug-disease pairs in drug repositioning, offering a novel computational method to expedite the drug discovery process.