Angiogenesis played a key role in the pathogenesis of various diseases,especially cancer,so the development of more rapid and efficient intelligent identification tools for anti-angiogenic peptides(AAPs)was particularly important.In this paper,a deep network voting identification model,iAAPs-DNV,was constructed based on multiple feature engineering,deep learning,and ensemble learning approaches.The feature information of amino acid sequences was extracted using AAindex coding,encoding based on grouped weights(EBGW),K-spacing amino acid pairs(KSAAP),second-order moving average(SOMA)derived from physicochemical properties,and BLOSUM62 coding.Subsequently,the soft voting strategy was employed to integrate the bidirectional long short-term memory network(BiLSTM)and the convolutional neural network(CNN),both of which incorporated the attention mechanism.Identification results were then outputted through a fully connected layer.The identification accuracies of iAAPs-DNV in the Main dataset and NT15 dataset were significantly superior to those of existing identification models,indicating that the model could efficiently and accurately identify AAPs.
关键词
抗血管生成肽/双向长短期记忆网络/卷积神经网络/软投票/注意力机制
Key words
anti-angiogenic peptides/bidirectional long-short term memory network/convolutional neural network/soft voting/attention
维普
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