Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction

TL;DR

This paper introduces a novel multi-view link prediction method called MKronRLSF-LP, which improves drug-side effect prediction by integrating multiple graph views and constraints, demonstrating superior performance on real datasets.

Contribution

The paper proposes MKronRLSF-LP, a new multi-view fusion-based link propagation method that enhances drug-side effect prediction by leveraging consensus partitions and graph Laplacian constraints.

Findings

Method outperforms existing approaches in accuracy.

Demonstrates robustness across datasets.

Effective integration of multi-view data.

Abstract

Drug-side effect prediction has become an essential area of research in the field of pharmacology. As the use of medications continues to rise, so does the importance of understanding and mitigating the potential risks associated with them. At present, researchers have turned to data-driven methods to predict drug-side effects. Drug-side effect prediction is a link prediction problem, and the related data can be described from various perspectives. To process these kinds of data, a multi-view method, called Multiple Kronecker RLS fusion-based link propagation (MKronRLSF-LP), is proposed. MKronRLSF-LP extends the Kron-RLS by finding the consensus partitions and multiple graph Laplacian constraints in the multi-view setting. Both of these multi-view settings contribute to a higher quality result. Extensive experiments have been conducted on drug-side effect datasets, and our empirical results provide evidence that our approach is effective and robust.