Predicting overlapping protein complexes in weighted interactome networks

Wen-yin NI; Hui-jun XIONG; Bi-hai ZHAO; Sai HU

doi:10.1631/jzus.C13b0097

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Predicting overlapping protein complexes in weighted interactome networks

计算机与自动化 | Updated：2022-12-09

- Predicting overlapping protein complexes in weighted interactome networks
- Predicting overlapping protein complexes in weighted interactome networks
- 信息与电子工程前沿（英文） 2013年14卷第10期页码：756-765
- Affiliations：
  
  Department of Information and Computing Science, Changsha University, Changsha 410003, China
- Author bio：
  
  [ "Wen-yin NI, E-mail:masonni@163.com" ]
  Sai HU, E-mail:husaiccsu@163.com
- Funds：
  
  Project supported by the Scientific Research Foundation of Hunan Province (No. 11C0125), the Scientific Planning Project of Hunan Province (No. XJK011CXJ002), and the Science and Technology Foundation of Changsha City (Nos. K1205049-11 and K1205048-11), China
- DOI：10.1631/jzus.C13b0097
  中图分类号： TP311;R857.3
- 收稿：2013-04-03，
  
  修回：2013-;9-23，
  
  纸质出版：2013-10
- Accepted：
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Predicting overlapping protein complexes in weighted interactome networks[J]. 信息与电子工程前沿（英文）, 2013,14(10):756-765.

Wen-yin NI, Hui-jun XIONG, Bi-hai ZHAO, et al. Predicting overlapping protein complexes in weighted interactome networks[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(10): 756-765.
Predicting overlapping protein complexes in weighted interactome networks[J]. 信息与电子工程前沿（英文）, 2013,14(10):756-765. DOI： 10.1631/jzus.C13b0097.

Wen-yin NI, Hui-jun XIONG, Bi-hai ZHAO, et al. Predicting overlapping protein complexes in weighted interactome networks[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(10): 756-765. DOI： 10.1631/jzus.C13b0097.

摘要

Abstract

Protein complexes play important roles in integrating individual gene products to perform useful cellular functions. The increasing mount of protein–protein interaction (PPI) data has enabled us to predict protein complexes. In spite of the advances in these computational approaches and experimental techniques

it is impossible to construct an absolutely reliable PPI network. Taking into account the reliability of interactions in the PPI network

we have constructed a weighted protein–protein interaction (WPPI) network

in which the reliability of each interaction is represented as a weight using the topology of the PPI network. As overlaps are likely to have biological importance

we proposed a novel method named WN-PC (weighted network-based method for predicting protein complexes) to predict overlapping protein complexes on the WPPI network. The proposed algorithm predicts neighborhood graphs with an aggregation coefficient over a threshold as candidate complexes

and binds attachment proteins to candidate complexes. Finally

we have filtered redundant complexes which overlap other complexes to a very high extent in comparison to their density and size. A comprehensive comparison between competitive algorithms and our WN-PC method has been made in terms of the

-measure

coverage rate

and

-value. We have applied WN-PC to two different yeast PPI data sets

one of which is a huge PPI network consisting of over 6000 proteins and 200 000 interactions. Experimental results show that WN-PC outperforms the state-of-the-art methods. We think that our research may be helpful for other applications in PPI networks.

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references

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