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Correlated Protein Function Prediction via Maximization of Data-Knowledge Consistency
- Source :
- Journal of computational biology : a journal of computational molecular cell biology. 22(6)
- Publication Year :
- 2015
-
Abstract
- 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.
- Subjects :
- Saccharomyces cerevisiae
Machine learning
computer.software_genre
Consistency (database systems)
Similarity (network science)
Protein Annotation
Protein Interaction Mapping
Genetics
Protein function prediction
Databases, Protein
Molecular Biology
Mathematics
business.industry
Perspective (graphical)
Computational Biology
Proteins
Pattern recognition
Molecular Sequence Annotation
Maximization
Function (mathematics)
Computational Mathematics
Computational Theory and Mathematics
Modeling and Simulation
Pairwise comparison
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- ISSN :
- 15578666
- Volume :
- 22
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Journal of computational biology : a journal of computational molecular cell biology
- Accession number :
- edsair.doi.dedup.....4be9640a2768f9e7f319c2fb73152cd2