Predicting irrelevant functions of proteins based on dimensionality reduction
Jun Wang, Guoxian Yu, Guangyuan Fu, Maozu Guo · Scientia Sinica Informationis · 2017
Proteins are the foundation for many life processes and accurately annotating their biological functions can significantly boost the development of life sciences. Current function prediction models focus on employing the knowledge that proteins perform specific functions (positive examples), but ignore the knowledge that some functions are irrelevant for a protein (negative examples). Recent research indicates that incorporating negative examples can reduce the complexity and improve the accuracy of protein function prediction. In this paper, we propose an approach for predicting irrelevant functions of proteins based on dimensionality reduction (IFDR). Initially, IFDR performs random walks through matrices in a protein-protein interactions (PPI) network, as well as the corresponding protein-function association matrices, in order to explore the underlying relationships between proteins and model the missing functional annotations of proteins. Next, IFDR uses single value decomposition to project these matrices into low-dimensional numerical matrices. Finally, IFDR uses semi-supervised regression to predict negative examples of proteins. Experiments on S. cerevisiae, H. sapiens, and A. thaliana data demonstrate that IFDR can more accurately predict negative examples when compared to related methods. Dimensionality reduction in the network space and label space can both improve the accuracy of negative example prediction.