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...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Yang, Li, Li-Ping, Wang, Lei, Yu, Chang-Qing, Wang, Zheng, You, Zhu-Hong
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