Abstract
The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. We further show that, in theory, eigenvalue transformation can be viewed as a feature transformation on the kernel matrix. Accordingly, although we only apply this technique to two algorithms in the current study, eigenvalue transformation also has the potential to be applied to other algorithms based on kernels.
Highlights
The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs
We propose an eigenvalue transformation technique and apply this technique to two representative algorithms based on kernels (RLS and supervised link prediction classifier (SLP))
For the Regularized Least Squares classifier (RLS) or SLP algorithm with eigenvalue transformation applied, if the regularization parameter σ is fixed, we can show that the object function of RLS or SLP can achieve the minimum value when the eigenvalue exponent α = 0
Summary
The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. Validating drug targets by experiments is expensive and time-consuming This consideration motivates the need to develop computational methods to predict drug-target interactions with high accuracy[1]. The experimental results show that algorithms to which eigenvalue transformation is applied achieved better performance than the original algorithms on drug-target interaction prediction, i.e., eigenvalue transformation is an efficient technique for improving performance in predicting drug-target interactions. We investigate how eigenvalue transformation influences algorithms, and several interesting results are presented
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