The performance of PrMFTP and their variants on the test set.

The highest value is highlighted in bold. w/o is abbreviation of without. The mean ± standard deviation on 5-fold cross-validation is shown for models. *, **, *** and **** mean that PrMFTP is significantly better at P-value < 0.05, P-value < 0.01, P-value < 0.001 and P-value < 0.0001 (t-test), respectively.</p

The performance of the base (CNN+BiLSTM+MHSA) model with different calculation class weight methods and MLSMOTE on the test set.

The highest value is highlighted in bold. On all performance metrics, Base+CW (our model) is significantly better compared with the other methods. The mean ± standard deviation on 5-fold cross-validation is shown for models. *, **, *** and **** mean that CNN+BiLSTM+MHSA (our model) is significantly better at P-value (DOCX)</p

The framework of PrMFTP.

First, peptide sequences are encoded as an input vector using numbers, and converted into a fixed-size matrix through the embedding layer. Second, DNN layer, a combination of multi-scale CNN and BiLSTM architectures, is used to capture the sequence features. Third, multi-head self-attention mechanism (MSHA) is used to make the model attend the more important and discriminating sequence features for prediction of multi-functional therapeutic peptides. Finally, the resulting feature matrix is fed into a classification layer and applied to score the different therapeutic peptides to achieve the predicted result.</p

Class weights calculated by different methods.

(PDF)</p

BBPpred : sequence-based prediction of blood-brain barrier peptides with feature representation learning and logistic regression

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