2013 CollaborativeMatrixFactorizatio

• (Zheng et al., 2013) ⇒ Xiaodong Zheng, Hao Ding, Hiroshi Mamitsuka, and Shanfeng Zhu. (2013). “Collaborative Matrix Factorization with Multiple Similarities for Predicting Drug-target Interactions.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487670

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Cited By

• http://scholar.google.com/scholar?q=%222013%22+Collaborative+Matrix+Factorization+with+Multiple+Similarities+for+Predicting+Drug-target+Interactions
• http://dl.acm.org/citation.cfm?id=2487575.2487670&preflayout=flat#citedby

Quotes

Author Keywords

• Chemoinformatics; drug-target interaction; knowledge acquisition; least squares approximation; multiple types of similarities over drug and targets; parameter learning; weighted low-rank approximation

Abstract

We address the problem of predicting new drug-target interactions from three inputs: known interactions, similarities over drugs and those over targets. This setting has been considered by many methods, which however have a common problem of allowing to have only one similarity matrix over drugs and that over targets. The key idea of our approach is to use more than one similarity matrices over drugs as well as those over targets, where weights over the multiple similarity matrices are estimated from data to automatically select similarities, which are effective for improving the performance of predicting drug-target interactions. We propose a factor model, named Multiple Similarities Collaborative Matrix Factorization (MSCMF), which projects drugs and targets into a common low-rank feature space, which is further consistent with weighted similarity matrices over drugs and those over targets. These two low-rank matrices and weights over similarity matrices are estimated by an alternating least squares algorithm. Our approach allows to predict drug-target interactions by the two low-rank matrices collaboratively and to detect similarities which are important for predicting drug-target interactions. This approach is general and applicable to any binary relations with similarities over elements, being found in many applications, such as recommender systems. In fact, MSCMF is an extension of weighted low-rank approximation for one-class collaborative filtering. We extensively evaluated the performance of MSCMF by using both synthetic and real datasets. Experimental results showed nice properties of MSCMF on selecting similarities useful in improving the predictive performance and the performance advantage of MSCMF over six state-of-the-art methods for predicting drug-target interactions.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 CollaborativeMatrixFactorizatio	Xiaodong Zheng Hao Ding Hiroshi Mamitsuka Shanfeng Zhu			Collaborative Matrix Factorization with Multiple Similarities for Predicting Drug-target Interactions				10.1145/2487575.2487670		2013