Title: PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction

Abstract

Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using an assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. Ultimately, this pipeline will be useful for predicting PPI in nonmodel species.

Authors:

Yao, Jianzhuang ^[1]; Guo, Hong ^[1]; Yang, Xiaohan ^[2]

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+ Show Author Affiliations

1. Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
2. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

Publication Date:

Thu Jan 01 00:00:00 EST 2015

Research Org.:

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1234001

Alternate Identifier(s):

OSTI ID: 1247926

Grant/Contract Number:  

DE–AC05–00OR22725; AC05-00OR22725

Resource Type:

Published Article

Journal Name:

International Journal of Genomics

Additional Journal Information:

Journal Name: International Journal of Genomics Journal Volume: 2015; Journal ID: ISSN 2314-436X

Publisher:

Hindawi Publishing Corporation

Country of Publication:

Egypt

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES