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

Joint Authors

Yao, Jianzhuang
Guo, Hong
Yang, Xiaohan

Source

International Journal of Genomics

Issue

Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2015-10-11

Country of Publication

Egypt

No. of Pages

7

Main Subjects

Biology

Abstract EN

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.

This pipeline will be useful for predicting PPI in nonmodel species.

American Psychological Association (APA)

Yao, Jianzhuang& Guo, Hong& Yang, Xiaohan. 2015. PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction. International Journal of Genomics،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1066033

Modern Language Association (MLA)

Yao, Jianzhuang…[et al.]. PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction. International Journal of Genomics No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1066033

American Medical Association (AMA)

Yao, Jianzhuang& Guo, Hong& Yang, Xiaohan. PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction. International Journal of Genomics. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1066033

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1066033