Journal
JOURNAL OF PROTEOME RESEARCH
Volume 16, Issue 5, Pages 2044-2053Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.7b00019
Keywords
cell-penetrating peptides; machine learning; feature representation; feature selection
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Funding
- National Natural Science Foundation of China [61370010]
- State Key Laboratory of Genetic Resources and Evolution [GREKF16-11]
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Cell-penetrating peptides (CPPs), have been proven as important drug delivery vehicles, demonstrating the potential as therapeutic candidates. The past decade has witnessed a rapid growth in CPP-based research. Recently, many computational efforts have been made to develop machine-learning-based methods for identifying CPPs. Although much progress has been made, existing methods still suffer low feature representation capability that limits further performance improvement. In this study, we propose a novel predictor called CPPred-RF, in which we integrate multiple sequence-based feature descriptors to sufficiently explore distinct information embedded in CPPs, employ a well-established feature selection technique to improve the feature representation, and, for the first time, construct a two-layer prediction framework based on the random forest algorithm. The jackknife results on benchmark data sets show that the proposed CPPred-RF is at least competitive with the state-of-the-art predictors. Moreover, we establish the first online Web server in terms of predicting CPPs and their uptake efficiency simultaneously. It is freely available at http:// server.malab.cn/CPPred-RF.
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