Computational Prediction of Protein Function Based on Weighted Mapping of Domains and GO Terms. (23rd April 2014)

• Record Type: Journal Article
• Authors: Teng, Zhixia; Guo, Maozu; Dai, Qiguo; Wang, Chunyu; Li, Jin; Liu, Xiaoyan
• Other Names: Zhao Xing-Ming Academic Editor.
• Is Part Of: BioMed research international. Volume 2014(2014)
• Publication Date: 2014-04-23
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2014/641469
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : In this paper, we propose a novel method, SeekFun, to predict protein function based on weighted mapping of domains and GO terms. Firstly, a weighted mapping of domains and GO terms is constructed according to GO annotations and domain composition of the proteins. The association strength between domain and GO term is weighted by symmetrical conditional probability. Secondly, the mapping is extended along the true paths of the terms based on GO hierarchy. Finally, the terms associated with resident domains are transferred to host protein and real annotations of the host protein are determined by association strengths. Our careful comparisons demonstrate that SeekFun outperforms the concerned methods on most occasions. SeekFun provides a flexible and effective way for protein function prediction. It benefits from the well-constructed mapping of domains and GO terms, as well as the reasonable strategy for inferring annotations of protein from those of its domains.