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Prediction of cell penetrating peptides and their uptake efficiency using random forest-based feature selections
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  • Peng Liu,
  • Yijie Ding,
  • Ying Rong,
  • Dong Chen
Peng Liu
University of Electronic Science and Technology of China

Corresponding Author:[email protected]

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Yijie Ding
University of Electronic Science and Technology of China
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Ying Rong
Beidahuang Industry Group General Hospital
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Dong Chen
Quzhou University
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Cell penetrating peptides (CPPs) are short peptides that can carry biomolecules of varying sizes across the cell membrane into the cytoplasm. Correctly identifying CPPs is the basis for studying their functions and mechanisms. Here, we propose a novel CPP predictor that is able to predict CPPs and their uptake efficiency. In our method, five feature descriptors are applied to encode the sequence and compose a hybrid feature vector. Afterward, the wrapper + random forest algorithm is employed, which combines feature selection with the prediction process to find features that are crucial for identifying CPPs. The jackknife cross validation result shows that our predictor is comparable to state-of-the-art CPP predictors, and our method reduces the feature dimension, which improves computational efficiency and avoids overfitting, allowing our predictor to be adopted to identify large-scale CPP data.
03 Dec 2021Submitted to AIChE Journal
07 Dec 2021Submission Checks Completed
07 Dec 2021Assigned to Editor
08 Dec 2021Reviewer(s) Assigned
18 Feb 2022Editorial Decision: Revise Major
02 Apr 20221st Revision Received
03 Apr 2022Submission Checks Completed
03 Apr 2022Assigned to Editor
03 Apr 2022Reviewer(s) Assigned
14 May 2022Editorial Decision: Accept
Sep 2022Published in AIChE Journal volume 68 issue 9. 10.1002/aic.17781