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Protein function prediction based on zero-one matrix factorization

Accurately annotating the functions of proteins is one of the key tasks of functional genomics. A large portion of functional annotations of proteins is missing, and the functional label space is expansive. Moreover, label compression methods have been proposed and applied to predict protein function; however, such methods lack the interpretability of compressed labels and suffer from the inherent problem of thresholding labels in multi-label learning. To solve these problems, this paper proposes a protein function prediction method based on zero-one matrix factorization (ZOMF). ZOMF first factorizes the protein-function association matrix into two low-rank zero-one matrices and explores the inner latent relationship between proteins and labels. Subsequently, it defines two smoothness terms on these two low-rank matrices with respect to protein–protein interactions and the structural relationships between labels to guide the optimization of low-rank matrices. Finally, to predict protein function, it reconstructs the association matrix using the optimized two low-rank matrices. Experimental results on four model species (yeast, Arabidopsis, mouse, and human) show that ZOMF can predict protein functions more accurately than existing algorithms. However, it does not need to threshold the reconstructed matrix, and the compressed zero-one labels have more than one intuitive explanation.