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Issue No.03 - May-June (2012 vol.9)
pp: 740-753
Dao-Qing Dai , Dept. of Math., Sun Yat-Sen Univ., Guangzhou, China
The functional annotation of proteins is one of the most important tasks in the post-genomic era. Although many computational approaches have been developed in recent years to predict protein function, most of these traditional algorithms do not take interrelationships among functional terms into account, such as different GO terms usually coannotate with some common proteins. In this study, we propose a new functional similarity measure in the form of Jaccard coefficient to quantify these interrelationships and also develop a framework for incorporating GO term similarity into protein function prediction process. The experimental results of cross-validation on S. cerevisiae and Homo sapiens data sets demonstrate that our method is able to improve the performance of protein function prediction. In addition, we find that small size terms associated with a few of proteins obtain more benefit than the large size ones when considering functional interrelationships. We also compare our similarity measure with other two widely used measures, and results indicate that when incorporated into function prediction algorithms, our proposed measure is more effective. Experiment results also illustrate that our algorithms outperform two previous competing algorithms, which also take functional interrelationships into account, in prediction accuracy. Finally, we show that our method is robust to annotations in the database which are not complete at present. These results give new insights about the importance of functional interrelationships in protein function prediction.
proteins, cellular biophysics, genomics, microorganisms, molecular biophysics, S.cerevisiae, protein function prediction algorithms, functional interrelationships, functional annotation, post-genomic era, traditional algorithms, functional similarity measurement, Jaccard coefficient, Homo sapiens data sets, cerevisiae data sets, Proteins, Prediction algorithms, Bioinformatics, Computational biology, Training data, Training, RNA, Gaussian random fields model., Protein function prediction, Gene Ontology, semantic similarity measure, protein-protein interaction
Dao-Qing Dai, "A Framework for Incorporating Functional Interrelationships into Protein Function Prediction Algorithms", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 3, pp. 740-753, May-June 2012, doi:10.1109/TCBB.2011.148
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