Next Article in Journal
Inhibition by Commercial Aminoglycosides of Human Connexin Hemichannels Expressed in Bacteria
Next Article in Special Issue
Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information
Previous Article in Journal
Hydroboration-Oxidation of (±)-(1α,3α,3aβ,6aβ) -1,2,3,3a,4,6a-Hexahydro-1,3-pentalenedimethanol and Its O-Protected Derivatives: Synthesis of New Compounds Useful for Obtaining (iso)Carbacyclin Analogues and X-ray Analysis of the Products
Previous Article in Special Issue
Predict the Relationship between Gene and Large Yellow Croaker’s Economic Traits
Open AccessArticle

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

1
School of Computer, Wuhan University, Wuhan 430072, China
2
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Molecules 2017, 22(12), 2056; https://doi.org/10.3390/molecules22122056
Received: 12 October 2017 / Revised: 19 November 2017 / Accepted: 20 November 2017 / Published: 25 November 2017
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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. View Full-Text
Keywords: drug-target interactions; label propagation; linear neighborhood; integrated information drug-target interactions; label propagation; linear neighborhood; integrated information
Show Figures

Figure 1

MDPI and ACS Style

Zhang, W.; Chen, Y.; Li, D. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information. Molecules 2017, 22, 2056.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop