Drug Repurposing Using Knowledge Graph Embeddings with a Focus on Vector-Borne Diseases: A Model Comparison

cic.institucionOrigenLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.isFulltextSI
cic.isPeerReviewedSI
cic.lugarDesarrolloLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.parentTypeObjeto de conferencia
cic.versionAceptada
dc.date.accessioned2023-08-28T16:47:20Z
dc.date.available2023-08-28T16:47:20Z
dc.identifier.urihttps://digital.cic.gba.gob.ar/handle/11746/12033
dc.titleDrug Repurposing Using Knowledge Graph Embeddings with a Focus on Vector-Borne Diseases: A Model Comparisonen
dc.typeDocumento de conferencia
dcterms.abstractVector-borne diseases carried by mosquitoes, ticks, and other vectors are among the fastest-spreading and most extensive diseases worldwide, mainly active in tropical regions. Also, in the context of the current climate change, these diseases are becoming a hazard for other climatic zones. Hence, drug repurposing methods can identify already approved drugs to treat them efficiently, reducing development costs and time. Knowledge graph embedding techniques can encode biological information in a single structure that allows users to operate relationships, extract information, learn connections, and make predictions to discover potential new relationships between existing drugs and vector-borne diseases. In this article, we compared seven knowledge graph embedding models (TransE, TransR, TransH, UM, DistMult, RESCAL, and ERMLP) applied to Drug Repurposing Knowledge Graph (DRKG), analyzing their predictive performance over seven different vector-borne diseases (dengue, chagas, malaria, yellow fever, leishmaniasis, filariasis, and schistosomiasis), measuring their embedding quality and external performance against a ground-truth. Our analysis found that no single predictive model consistently outperformed all others across all diseases and proposed different strategies to improve predictive performance.en
dcterms.creator.authorDiego López Yse
dcterms.creator.authorDiego Torres
dcterms.extent105-117
dcterms.identifier.otherDOI: 10.1007/978-3-031-40942-4_8
dcterms.identifier.otherISBN: 978-3-031-40942-4
dcterms.isPartOf.issue11th Conference JCC-BD&ET 2023 (La Plata, 27 al 29 de junio de 2023)
dcterms.isPartOf.itemCloud Computing, Big Data & Emerging Topics
dcterms.issued2023-08-11
dcterms.languageInglés
dcterms.licenseAttribution-NonCommercial-ShareAlike 4.0 International (BY-NC-SA 4.0)
dcterms.subjectMachine Learningen
dcterms.subjectKnowledge Graphsen
dcterms.subjectKnowledge Graph Embeddingsen
dcterms.subjectDrug repurposingen
dcterms.subjectVector-borne diseasesen
dcterms.subjectBiotechnologyen
dcterms.subject.materiaCiencias de la Computación e Información

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Drug repurposing using knowledge.pdf-PDFA.pdf
Tamaño:
279.04 KB
Formato:
Adobe Portable Document Format

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
3.46 KB
Formato:
Item-specific license agreed upon to submission
Descripción: