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An efficient method for protein function annotation based on multilayer protein networks

Overview of attention for article published in Human Genomics, September 2016
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Title
An efficient method for protein function annotation based on multilayer protein networks
Published in
Human Genomics, September 2016
DOI 10.1186/s40246-016-0087-x
Pubmed ID
Authors

Bihai Zhao, Sai Hu, Xueyong Li, Fan Zhang, Qinglong Tian, Wenyin Ni

Abstract

Accurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Many computational methods based on protein-protein interaction (PPI) networks have been proposed to predict the function of proteins. However, the precision of these predictions still needs to be improved, due to the incompletion and noise in PPI networks. Integrating network topology and biological information could improve the accuracy of protein function prediction and may also lead to the discovery of multiple interaction types between proteins. Current algorithms generate a single network, which is archived using a weighted sum of all types of protein interactions. The influences of different types of interactions on the prediction of protein functions are not the same. To address this, we construct multilayer protein networks (MPN) by integrating PPI networks, the domain of proteins, and information on protein complexes. In the MPN, there is more than one type of connections between pairwise proteins. Different types of connections reflect different roles and importance in protein function prediction. Based on the MPN, we propose a new protein function prediction method, named function prediction based on multilayer protein networks (FP-MPN). Given an un-annotated protein, the FP-MPN method visits each layer of the MPN in turn and generates a set of candidate neighbors with known functions. A set of predicted functions for the testing protein is then formed and all of these functions are scored and sorted. Each layer plays different importance on the prediction of protein functions. A number of top-ranking functions are selected to annotate the unknown protein. The method proposed in this paper was a better predictor when used on Saccharomyces cerevisiae protein data than other function prediction methods previously used. The proposed FP-MPN method takes different roles of connections in protein function prediction into account to reduce the artificial noise by introducing biological information.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 9 26%
Student > Master 3 9%
Researcher 3 9%
Student > Ph. D. Student 2 6%
Lecturer > Senior Lecturer 1 3%
Other 2 6%
Unknown 14 41%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 26%
Computer Science 4 12%
Agricultural and Biological Sciences 3 9%
Pharmacology, Toxicology and Pharmaceutical Science 2 6%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 14 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 September 2016.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from Human Genomics
#463
of 564 outputs
Outputs of similar age
#256,236
of 330,830 outputs
Outputs of similar age from Human Genomics
#7
of 7 outputs
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