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Predicting essential proteins based on subcellular localization, orthology and PPI networks

Overview of attention for article published in BMC Bioinformatics, August 2016
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Title
Predicting essential proteins based on subcellular localization, orthology and PPI networks
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1115-5
Pubmed ID
Authors

Gaoshi Li, Min Li, Jianxin Wang, Jingli Wu, Fang-Xiang Wu, Yi Pan

Abstract

Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods. Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION. We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 19%
Researcher 4 13%
Student > Bachelor 3 9%
Student > Master 3 9%
Lecturer 2 6%
Other 6 19%
Unknown 8 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 19%
Computer Science 5 16%
Agricultural and Biological Sciences 4 13%
Medicine and Dentistry 2 6%
Neuroscience 2 6%
Other 3 9%
Unknown 10 31%
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 03 September 2016.
All research outputs
#20,340,423
of 22,886,568 outputs
Outputs from BMC Bioinformatics
#6,871
of 7,298 outputs
Outputs of similar age
#294,484
of 337,459 outputs
Outputs of similar age from BMC Bioinformatics
#125
of 136 outputs
Altmetric has tracked 22,886,568 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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