Title
Bbppred: Sequence-Based Prediction Of Blood-Brain Barrier Peptides With Feature Representation Learning And Logistic Regression
Abstract
Blood-brain barrier peptides (BBPs) have a large range of biomedical applications since they can cross the blood-brain barrier based on different mechanisms. As experimental methods for the identification of BBPs are laborious and expensive, computational approaches are necessary to be developed for predicting BBPs. In this work, we describe a computational method, BBPpred (blood-brain barrier peptides prediction), that can efficiently identify BBPs using logistic regression. We investigate a wide variety of features from amino acid sequence information, and then a feature learning method is adopted to represent the informative features. To improve the prediction performance, seven informative features are selected for classification by eliminating redundant and irrelevant features. In addition, we specifically create two benchmark data sets (training and independent test), which contain a total of 119 BBPs from public databases and the literature. On the training data set, BBPpred shows promising performances with an AUC score of 0.8764 and an AUPR score of 0.8757 using the 10-fold cross-validation. We also test our new method on the independent test data set and obtain a favorable performance. We envision that BBPpred will be a useful tool for identifying, annotating, and characterizing BBPs. BBPpred is freely available at http://BBPpred.xialab.info.
Year
DOI
Venue
2021
10.1021/acs.jcim.0c01115
JOURNAL OF CHEMICAL INFORMATION AND MODELING
DocType
Volume
Issue
Journal
61
1
ISSN
Citations 
PageRank 
1549-9596
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Ruyu Dai100.34
Wei Zhang215627.96
Wending Tang300.34
Evelien Wynendaele400.34
Qizhi Zhu501.35
Yannan Bin600.34
Bart De Spiegeleer7111.44
Junfeng Xia814420.14