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

Autor:	Qian, Yuqing, Zheng, Ziyu, Tiwari, Prayag, Ding, Yijie, Zou, Quan
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Transactions on Machine Learning Research (TMLR 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.00105 Zobrazit plný text záznamu View this record from Arxiv