{'fraction_non_alphanumeric': 0.02710413694721826, 'fraction_numerical': 0.0, 'mean_word_length': 5.435779816513762, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
GONET: A Deep Network to Annotate Proteins via Recurrent Convolution Networks

Finding out the functions of protein in life activities precisely is nontrivial, which is the core of current proteomics research. Gene Ontology standardizes the function of protein into a series of GO terms, each of which belongs to exactly one of the three subontologies: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The prediction of protein function can be considered as a multi-label classification problem. Traditional methods often spend a lot of costs to extract handcrafted features and plenty of domain knowledge is needed when solving these tasks, while using deep learning technology can overcome these shortcomings. Here, we propose a deep model GONET based on recurrent convolutional neural networks, which annotates protein in an end-to-end manner. Our model combines protein sequences and protein-protein interaction (PPI) network data, and utilizes representation learning to learn distributed representation of proteins to overcome the sparse nature and semantic independence problem. Moreover, we adopt a quite deep CNNRNN-Attention model, which is able to effectively extract high-order features of protein sequences. We have carried out experiments on several datasets, which achieve the state-of-the-art in some metrics compared with the existing competitive methods.