Protein function prediction is a fundamental task in the post-genomic era. Available functional annotations of proteins are incomplete and the annotations of two homologous species are complementary to each other. However, how to effectively leverage mutually complementary annotations of different species to further boost the prediction performance is still not well studied. In this paper, we propose a cross-species protein function prediction approach by performing Asynchronous Random Walk on a heterogeneous network (AsyRW). AsyRW firstly constructs a heterogeneous network to integrate multiple functional association networks derived from different biological data, established homology-relationships between proteins from different species, known annotations of proteins and Gene Ontology (GO). To account for the intrinsic structures of intra- and inter-species of proteins and that of GO, AsyRW quantifies the individual walk lengths of each network node using the gravity-like theory and performs asynchronous-random walk with the individual length to predict associations between proteins and GO terms. Experiments on annotations archived in different years show that individual walk length and asynchronous-random walk can effectively leverage the complementary annotations of different species, AsyRW has a significantly improved performance to other related and competitive methods. The codes of AsyRW are available at: http://mlda.swu.edu.cn/codes.php?name=AsyRW.
|Original language||English (US)|
|Number of pages||1|
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|State||Published - Sep 24 2019|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work is supported by Natural Science Foundation of China (61741217, 61872300, 61873214, 61871020,
61571163 and 61532014), Fundamental Research Funds for the Central Universities (XDJK2019B024), the National Key Research and Development Plan Task of China (Grant No. 2016YFC0901902), Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228).