Correlated Protein Function Prediction via Maximization of Data-Knowledge Consistency
Hua Wang, Heng Huang, Chris Ding
RECOMB - 2014
Protein function prediction in conventional computational approaches is usually conducted one function at a time, fundamentally. As a result, the functions are treated as separate target classes. However, biological processes are highly correlated, which makes functions assigned to proteins are not independent. Therefore, it would be beneficial to make use of function category correlations in predicting protein function. 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 elegantly incorporated through the knowledge similarity. Comprehensive experimental evaluations on Saccharomyces cerevisiae data demonstrate promising results that validate the performance of our methods.
Links
- View publications from Hua Wang
- View publications presented in RECOMB
- View publications researching Matrix/Tensor Factorization
- View publications researching Multiple-Label Learning
- View publications applied to Bioinformatics
Cite this paper
MLA
Wang, Hua, Heng Huang, and Chris Ding. "Correlated protein function prediction via maximization of data-knowledge consistency." Journal of Computational Biology 22.6 (2015): 546-562.
BibTeX
@article{wang2015correlated,
title={Correlated protein function prediction via maximization of data-knowledge consistency},
author={Wang, Hua and Huang, Heng and Ding, Chris},
journal={Journal of Computational Biology},
volume={22},
number={6},
pages={546--562},
year={2015},
publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}
}