Log In Sign Up

Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure

by   Islam Akef Ebeid, et al.

Knowledge Graphs have been one of the fundamental methods for integrating heterogeneous data sources. Integrating heterogeneous data sources is crucial, especially in the biomedical domain, where central data-driven tasks such as drug discovery rely on incorporating information from different biomedical databases. These databases contain various biological entities and relations such as proteins (PDB), genes (Gene Ontology), drugs (DrugBank), diseases (DDB), and protein-protein interactions (BioGRID). The process of semantically integrating heterogeneous biomedical databases is often riddled with imperfections. The quality of data-driven drug discovery relies on the accuracy of the mining methods used and the data's quality as well. Thus, having complete and refined biomedical knowledge graphs is central to achieving more accurate drug discovery outcomes. Here we propose using the latest graph representation learning and embedding models to refine and complete biomedical knowledge graphs. This preliminary work demonstrates learning discrete representations of the integrated biomedical knowledge graph Chem2Bio2RD [3]. We perform a knowledge graph completion and refinement task using a simple top-K cosine similarity measure between the learned embedding vectors to predict missing links between drugs and targets present in the data. We show that this simple procedure can be used alternatively to binary classifiers in link prediction.


page 1

page 2

page 3

page 4


A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

Drug discovery and development is an extremely complex process, with hig...

A knowledge graph representation learning approach to predict novel kinase-substrate interactions

The human proteome contains a vast network of interacting kinases and su...

Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

Improving on the standard of care for diseases is predicated on better t...

Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

In this work, we provide a new formulation for Graph Convolutional Neura...

Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study

Biomedical knowledge graphs (KGs) hold rich information on entities such...

Learning to Discover Medicines

Discovering new medicines is the hallmark of human endeavor to live a be...