Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

Xiuquan Du, Xinrui Li, Hanqian Zhang and Yanping Zhang

Mathematical Problems in Engineering, 2015, vol. 2015, 1-7

Abstract:

Protein-protein interactions (PPIs) play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC) for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the -regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM) and much higher than naive Bayes (NB), neural networks (NN), and k -nearest neighbor (KNN). The result shows that the MSRC is efficient for PPIs prediction.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:858256

DOI: 10.1155/2015/858256

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