DeepAdd: Protein function prediction from k-mer embedding and additional features

Zhihua Du, Yufeng He, Jianqiang Li, Vladimir N. Uversky

Abstract: Highlights•DeepAdd is proposed to predict protein functions using a deep convolutional neural network (CNN) framework.•DeepAdd utilizes a Word2Vec method on defining the set of features to represent a protein.•DeepAdd consists of two CNN models with multiple convolution blocks that map the presented protein sequence to two-feature vectors representation. One feature representation is for the sequence similarity profile by SSP model. The other feature representation is the PPI network by PPI model.