Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

Remzi Çelebi; H. Turgut Uyar; Erkan Yasar; Özgür Gümüş; Oğuz Dikenelli; Michel Dumontier

doi:10.1186/s12859-019-3284-5

ScienceGate Book Chapters

JOURNAL ARTICLE

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

Remzi Çelebi H. Turgut Uyar Erkan Yasar Özgür Gümüş Oğuz Dikenelli Michel Dumontier

Year: 2019 Journal: BMC Bioinformatics Vol: 20 (1)Pages: 726-726 Publisher: BioMed Central

DOI: 10.1186/s12859-019-3284-5

Get Full-Text PDF Get Analytical Report

Keywords:

DrugBank Computer science Drug Drug-drug interaction Machine learning Disjoint sets Naive Bayes classifier Data mining Medicine Pharmacology Support vector machine Mathematics

Metrics

101

Cited By

11.57

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Computational Drug Discovery Methods

Physical Sciences → Computer Science → Computational Theory and Mathematics

Bioinformatics and Genomic Networks

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Biomedical Text Mining and Ontologies

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

Metrics

Citation History

Topics

Related Documents

Evaluation of Knowledge Graph Embedding Approaches for Drug-Drug Interaction Prediction using Linked Open Data

Evaluation of Knowledge Graph Embedding Approaches for Drug-Drug Interaction Prediction using Linked Open Data

Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Drug–target interaction prediction using knowledge graph embedding

Enhancing drug–drug interaction prediction by three-way decision and knowledge graph embedding