| swMATH ID: |
34761
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| Software Authors: |
Tian, Baoguang; Wu, Xue; Chen, Cheng; Qiu, Wenying; Ma, Qin; Yu, Bin
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| Description: |
Predicting protein-protein interactions by fusing various Chou’s pseudo components and using wavelet denoising approach. Research on protein-protein interactions (PPIs) not only helps to reveal the nature of life activities but also plays a driving role in understanding the mechanisms of disease activity and the development of effective drugs. The rapid development of machine learning provides new opportunities and challenges for understanding the mechanism of PPIs. It plays an important role in the field of proteomics research. In recent years, an increasing number of computational methods for predicting PPIs have been developed. This paper proposes a new method for predicting PPIs based on multi-information fusion. First, the pseudo-amino acid composition (PseAAC), auto-covariance (AC) and encoding based on grouped weight (EBGW) methods are used to extract the features of protein sequences, and the extracted three groups of feature vectors were fused. Secondly, the fused feature vectors are denoised by two-dimensional (2-D) wavelet denoising. Finally, the denoised feature vectors are input to the support vector machine (SVM) classifier to predict the PPIs. The ACC of PPIs of extit{Helicobacter pylori} ( extit{H. pylori}) and extit{Saccharomyces cerevisiae} datasets were 95.97 |
| Homepage: |
https://www.sciencedirect.com/science/article/abs/pii/S0022519318305642
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| Source Code: |
 https://github.com/QUST-AIBBDRC/PPIs-WDSVM/
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| Keywords: |
protein-protein interactions;
pseudo-amino acid composition;
multi-information fusion;
two-dimensional wavelet denoising;
support vector machine;
machine learning
|
| Related Software: |
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pLoc-mHum;
iRNA-3typeA;
HIVcleave;
iHSP-PseRAAAC;
pLoc-mPlant;
pLoc-mGneg
|
| Cited in: |
1 Document
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