Deep Learning in Antimicrobial Peptide Prediction.

Antimicrobial peptides (AMPs) have garnered significant attention from researchers as effective alternatives to antibiotics. In recent years, deep learning has demonstrated unique advantages in AMP prediction, surpassing traditional machine learning methods and offering new avenues to address the issue of antibiotic resistance. This review introduces the research foundations of deep learning in AMP prediction, covering data set status, processing methods, and representation learning approaches.

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

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.