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Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only &lt;1% of &gt;70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multilabel classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore, homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-called difficult proteins, which have &lt;60% sequence identity to proteins with annotations already. Thus, the vital and challenging problem now is how to develop a method for SAFP, particularly for difficult proteins.

GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank