NDLI: A Novel Feature Extraction Scheme with Ensemble Coding for Protein

Content Provider	PubMed Central
Author	Du, Xiuquan Cheng, Jiaxing Zheng, Tingting Duan, Zheng Qian, Fulan
Copyright Year	2014
Abstract	Protein–protein interactions (PPIs) play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in functional genomics. Although PPI data have been increasing because of the development of high-throughput technologies and computational methods, many problems are still far from being solved. In this study, a novel predictor was designed by using the Random Forest (RF) algorithm with the ensemble coding (EC) method. To reduce computational time, a feature selection method (DX) was adopted to rank the features and search the optimal feature combination. The DXEC method integrates many features and physicochemical/biochemical properties to predict PPIs. On the Gold Yeast dataset, the DXEC method achieves 67.2% overall precision, 80.74% recall, and 70.67% accuracy. On the Silver Yeast dataset, the DXEC method achieves 76.93% precision, 77.98% recall, and 77.27% accuracy. On the human dataset, the prediction accuracy reaches 80% for the DXEC-RF method. We extended the experiment to a bigger and more realistic dataset that maintains 50% recall on the Yeast All dataset and 80% recall on the Human All dataset. These results show that the DXEC method is suitable for performing PPI prediction. The prediction service of the DXEC-RF classifier is available at http://ailab.ahu.edu.cn:8087/DXECPPI/index.jsp.
Related Links	http://dx.doi.org/10.3390/ijms150712731
Ending Page	12749
Starting Page	12731
File Format	PDF
ISSN	14220067
e-ISSN	14220067
Journal	International Journal of Molecular Sciences
Issue Number	7
Volume Number	15
Language	English
Publisher	MDPI
Publisher Date	2014-07-18
Access Restriction	Open
Rights Holder	MDPI
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Spectroscopy Organic Chemistry Medicine Molecular Biology Physical and Theoretical Chemistry Catalysis Inorganic Chemistry Computer Science Applications

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