predicting drug-target interactions via within-score and between-score

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ID: 139562
2015
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Abstract
Network inference and local classification models have been shown to be useful in predicting newly potential drug-target interactions (DTIs) for assisting in drug discovery or drug repositioning. The idea is to represent drugs, targets, and their interactions as a bipartite network or an adjacent matrix. However, existing methods have not yet addressed appropriately several issues, such as the powerless inference in the case of isolated subnetworks, the biased classifiers derived from insufficient positive samples, the need of training a number of local classifiers, and the unavailable relationship between known DTIs and unapproved drug-target pairs (DTPs). Designing more effective approaches to address those issues is always desirable. In this paper, after presenting better drug similarities and target similarities, we characterize each DTP as a feature vector of within-scores and between-scores so as to hold the following superiorities: (1) a uniform vector of all types of DTPs, (2) only one global classifier with less bias benefiting from adequate positive samples, and (3) more importantly, the visualized relationship between known DTIs and unapproved DTPs. The effectiveness of our approach is finally demonstrated via comparing with other popular methods under cross validation and predicting potential interactions for DTPs under the validation in existing databases.
Reference Key
shi2015biomedpredicting Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jian-Yu Shi;Zun Liu;Hui Yu;Yong-Jun Li
Journal spectrochimica acta - part a: molecular and biomolecular spectroscopy
Year 2015
DOI
10.1155/2015/350983
URL
http://dx.doi.org/10.1155/2015/350983
Keywords
Medicine

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