Cargando…
An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model
Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein–protein intera...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679202/ https://www.ncbi.nlm.nih.gov/pubmed/31319578 http://dx.doi.org/10.3390/ijms20143511 |
_version_ | 1783441283830775808 |
---|---|
author | Li, Yang Li, Li-Ping Wang, Lei Yu, Chang-Qing Wang, Zheng You, Zhu-Hong |
author_facet | Li, Yang Li, Li-Ping Wang, Lei Yu, Chang-Qing Wang, Zheng You, Zhu-Hong |
author_sort | Li, Yang |
collection | PubMed |
description | Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein–protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs. |
format | Online Article Text |
id | pubmed-6679202 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66792022019-08-19 An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model Li, Yang Li, Li-Ping Wang, Lei Yu, Chang-Qing Wang, Zheng You, Zhu-Hong Int J Mol Sci Article Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein–protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs. MDPI 2019-07-17 /pmc/articles/PMC6679202/ /pubmed/31319578 http://dx.doi.org/10.3390/ijms20143511 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Yang Li, Li-Ping Wang, Lei Yu, Chang-Qing Wang, Zheng You, Zhu-Hong An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model |
title | An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model |
title_full | An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model |
title_fullStr | An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model |
title_full_unstemmed | An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model |
title_short | An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model |
title_sort | ensemble classifier to predict protein–protein interactions by combining pssm-based evolutionary information with local binary pattern model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679202/ https://www.ncbi.nlm.nih.gov/pubmed/31319578 http://dx.doi.org/10.3390/ijms20143511 |
work_keys_str_mv | AT liyang anensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT liliping anensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT wanglei anensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT yuchangqing anensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT wangzheng anensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT youzhuhong anensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT liyang ensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT liliping ensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT wanglei ensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT yuchangqing ensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT wangzheng ensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel AT youzhuhong ensembleclassifiertopredictproteinproteininteractionsbycombiningpssmbasedevolutionaryinformationwithlocalbinarypatternmodel |