AAPred-CNN: Accurate predictor based on deep convolution neural network for identification of anti-angiogenic peptides.

• In this study, we propose the first novel predictor named AAPred-CNN based on deep learning models and embedding techniques for the classification of AAP, attempting to apply deep model into this field and make improvements on the precondition performance. • AAPred-CNN is derived from TextCNN (Text Convolution Neural Network), adpoting multiple convolution channels to extract the local features of the input sequences. • Comparative results show that AAPred-CNN outperforms the state-of-the-art methods in all evaluation metrics and gains a remarkable improvement. • Notably, AAPred-CNN can work well even though only hundreds of training samples are given, which is intuitive. Recently, deep learning techniques have been developed for various bioactive peptide prediction tasks. However, there are only conventional machine learning-based methods for the prediction of anti-angiogenic peptides (AAP), which play an important role in cancer treatment. The main reason why no deep learning method has been involved in this field is that there are too few experimentally validated AAPs to support the training of deep models but researchers have believed that deep learning seriously depends on the amounts of labeled data. In this paper, as a tentative work, we try to predict AAP by constructing different classical deep learning models and propose the first deep convolution neural network-based predictor (AAPred-CNN) for AAP. Contrary to intuition, the experimental results show that deep learning models can achieve superior or comparable performance to the state-of-the-art model, although they are given a few labeled sequences to train. We also decipher the influence of hyper-parameters and training samples on the performance of deep learning models to help understand how the model work. Furthermore, we also visualize the learned embeddings by dimension reduction to increase the model interpretability and reveal the residue propensity of AAP through the statistics of convolutional features for different residues. In summary, this work demonstrates the powerful representation ability of AAPred-CNNfor AAP prediction, further improving the prediction accuracy of AAP. [ABSTRACT FROM AUTHOR]