Correlated Protein Function Prediction via Maximization of Data-Knowledge Consistency.
Conventional computational approaches for protein function prediction usually predict one function at a time, fundamentally. As a result, the protein functions are treated as separate target classes. However, biological processes are highly correlated in reality, which makes multiple functions assigned to a protein not independent. Therefore, it would be beneficial to make use of function category correlations when predicting protein functions. In this article, we propose a novel Maximization of Data-Knowledge Consistency (MDKC) approach to exploit function category correlations for protein function prediction. Our approach banks on the assumption that two proteins are likely to have large overlap in their annotated functions if they are highly similar according to certain experimental data. We first establish a new pairwise protein similarity using protein annotations from knowledge perspective. Then by maximizing the consistency between the established knowledge similarity upon annotations and the data similarity upon biological experiments, putative functions are assigned to unannotated proteins. Most importantly, function category correlations are gracefully incorporated into our learning objective through the knowledge similarity. Comprehensive experimental evaluations on the Saccharomyces cerevisiae species have demonstrated promising results that validate the performance of our methods.
1Department of Electrical Engineering and Computer Science, Colorado School of Mines, Golden, Colorado.
Hua
H
Wang
2Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas.
Heng
H
Huang
2Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas.
Chris
C
Ding
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D058977:Molecular Sequence Annotation / Q000379:methods; D025941:Protein Interaction Mapping / Q000379:methods; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism
D011506:Proteins
protein function prediction; symmetric nonnegative matrix factorization