Skip to content

rcelebi/GraphEmbedding4DDI

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.

GraphEmbedding4DDI

In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We have applied Logistic Regression, Naive Bayes and Random Forest on Drugbank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. We also propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs.

We performed cross-validation using different setting:

Traditional CV:

  • ddi_predict_traditional.ipynb

Proposed disjoint CV:

  • ddi_predict_disjoint.ipynb

Time-slice CV:

  • ddi_predict_timeslice.ipynb

To cite this work

Celebi, Remzi, Huseyin Uyar, Erkan Yasar, Ozgur Gumus, Oguz Dikenelli, and Michel Dumontier. "Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings." BMC bioinformatics 20, no. 1 (2019): 1-14.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published