Predicting Protein–Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence

Protein–protein interactions (PPIs) play an essential role in many biological cellular functions. However, it is still tedious and time-consuming to identify protein–protein interactions through traditional experimental methods. For this reason, it is imperative and necessary to develop a computatio...

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Bibliographic Details
Published in:Biology (Basel, Switzerland) Switzerland), 2022-06, Vol.11 (7), p.995
Main Authors: Zhan, Xinke, Xiao, Mang, You, Zhuhong, Yan, Chenggang, Guo, Jianxin, Wang, Liping, Sun, Yaoqi, Shang, Bingwan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Amino acid sequence
Classification
Computer applications
Datasets
Eigenvalues
Gene expression
KNN
locality preserving projections
Methods
Principal components analysis
Protein interaction
Proteins
Proteomics
PSSM
rotation forest
Signal transduction
Support vector machines
SVM
Wavelet transforms
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Summary:Protein–protein interactions (PPIs) play an essential role in many biological cellular functions. However, it is still tedious and time-consuming to identify protein–protein interactions through traditional experimental methods. For this reason, it is imperative and necessary to develop a computational method for predicting PPIs efficiently. This paper explores a novel computational method for detecting PPIs from protein sequence, the approach which mainly adopts the feature extraction method: Locality Preserving Projections (LPP) and classifier: Rotation Forest (RF). Specifically, we first employ the Position Specific Scoring Matrix (PSSM), which can remain evolutionary information of biological for representing protein sequence efficiently. Then, the LPP descriptor is applied to extract feature vectors from PSSM. The feature vectors are fed into the RF to obtain the final results. The proposed method is applied to two datasets: Yeast and H. pylori, and obtained an average accuracy of 92.81% and 92.56%, respectively. We also compare it with K nearest neighbors (KNN) and support vector machine (SVM) to better evaluate the performance of the proposed method. In summary, all experimental results indicate that the proposed approach is stable and robust for predicting PPIs and promising to be a useful tool for proteomics research.
ISSN:2079-7737
2079-7737
DOI:10.3390/biology11070995