Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information

Abstract

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

“…Since Equation (6) maps drugs and diseases into a lowdimensional space, a natural idea occurs that the lowdimensional representations should preserve the underlying interconnection information of drugs and diseases. Studies on manifold learning (Belkin et al, 2006;Ma and Fu, 2012;Zhang et al, 2018a), spectral graph theory (Chung, 1997;Rana et al, 2015) and their applications (Zhang et al, 2016a(Zhang et al, , 2017a(Zhang et al, ,b,c, 2018bRuan et al, 2017) have shown that the learning performance can be signally enhanced, if the local topological invariant properties are preserved. Cai et al (2011) proposed Laplacian regularizations to achieve this goal.…”

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

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