Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence
Joint Authors
You, Zhu-Hong
Gao, Xin
Huang, Yu-An
Wong, Leon
Wang, Lirong
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network.
Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate.
For this reason, computational methods are drawing more and more attention for predicting PPIs.
In this study, we report a computational method for predicting PPIs using the information of protein sequences.
The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC).
When performing on the PPIs dataset of Yeast, Human, and H.
pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods.
Promising results obtained have proven that the proposed method is feasible, robust, and powerful.
To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier.
Extensive experiments were also performed in which we used Yeast PPIs samples as training set to predict PPIs of other five species datasets.
American Psychological Association (APA)
Huang, Yu-An& You, Zhu-Hong& Gao, Xin& Wong, Leon& Wang, Lirong. 2015. Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence. BioMed Research International،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1057169
Modern Language Association (MLA)
Huang, Yu-An…[et al.]. Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence. BioMed Research International No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1057169
American Medical Association (AMA)
Huang, Yu-An& You, Zhu-Hong& Gao, Xin& Wong, Leon& Wang, Lirong. Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1057169
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057169