Algorithms and tools for protein–protein interaction networks clustering, with a special focus on population-based stochastic methods Clara Pizzuti1,† and Simona E. Rombo2,*,†

Algorithms and tools for protein–protein interaction networks clustering, with a special focus on population-based stochastic methods

Clara Pizzuti¹,^† and 

Simona E. Rombo, Bioinformatics 2014.

http://bioinformatics.oxfordjournals.org/content/30/10/1343.short


PR14 used 3 yeast PPI data to compare MCL with others. The MCL parameter was taken from Boheree2006. PR14 used protein complex as 'golden standard'. When overlapping score > 20%, MCL is the best algorithm. Bader's MCODE is also a good method for certain parameter settings.

Wang, Li, Deng, Pan, BMC review on clustering methods for protein interaction networks.

Recent advances in clustering methods for protein interaction networks

Jianxin Wang1,2*, Min Li1*, Youping Deng3, Yi Pan2

From The ISIBM International Joint Conference on Bioinformatics, Systems Biology and Intelligent

Computing (IJCBS), Shanghai, China. 3-8 August 2009

http://www.biomedcentral.com/content/pdf/1471-2164-11-S3-S10.pdf

cited by
http://scholar.google.com/scholar?cites=16432683922097612422&as_sdt=5,43&sciodt=0,43&hl=en

Reviewed 20 clustering methods, including MCL. MCL is commented as the highly successful.

10. Brohée S, van Helden J: Evaluation of clustering algorithms for proteinprotein

interaction networks. BMC Bioinformatics 2006, 7:48.

63. Vlasblom J, Wodak SJ: Markov clustering versus affinity propagation for

the partitioning of protein interaction graphs. BMC Bioinformatics 2009,10:99.

Lin C, Cho Y-R, Hwang W-C, Pei P, and Zhang A. 2007. Clustering Methods in a Protein–Protein Interaction Network. In: Hu X, and Pan Y, eds. Knowledge Discovery in Bioinformatics: John Wiley & Sons, Inc., 319-355.

CLUSTERING METHODS IN PROTEIN-PROTEIN INTERACTION NETWORK
Chuan Lin, Young-rae Cho, Woo-chang Hwang, Pengjun Pei, Aidong Zhang
Department of Computer Science and Engineering
State University of New York at Buffalo

Cite as:
Lin C, Cho Y-R, Hwang W-C, Pei P, and Zhang A. 2007. Clustering Methods in a Protein–Protein Interaction Network. In: Hu X, and Pan Y, eds. Knowledge Discovery in Bioinformatics: John Wiley & Sons, Inc., 319-355.

This review article did not provide enough details on validation and comparison of different algorithms.

Xue 07, MSB, a modular network model of aging

My notes on Xue 2007, a modular network model of aging.

Xue07 examined PPI in fruit fly and human brain aging. Xue07 used simulation and stated that "aging might preferentially attack key regulatory nodes that are important for the network stability, implicating a potential molecular basis for the stochastic nature of aging".

Xue07 identified negative (N) and positive (P) correlations during aging based on expression data, and then identified the PPI subnetwork for these NP correlations. Xue07 then used clustering method to identify 'network modules'.

Its method paper is in

Xia K, Dong D, Xue H, Zhu S,Wang J, Zhang Q, Hou L, Chen H, Tao R, Huang Z, Fu Z, Chen YG, Han JD (2006) Identification of the proliferation/differentiation switch in the cellular network of

multicellular organisms. PLoS Comput Biol 2: e145

Summary of network clustering methods, in progress

Network clustering is discussed in graph-based models in Wikipedia's entry on Clustering analysis.

• Hierarchical clustering using distance matrix
• Xu,Xiaowei, Yuruk, Nurcan, Feng Zhidan, Schweiger Thomas, SCAN: A structural clustering algotihm for networks. 
• Min-max cut method, C. Ding, X. He, H. Zha, M. Gu, and H. Simon, “A min-max
  cut algorithm for graph partitioning and data clustering”, Proc. of ICDM 2001.
• Normalized cut, J. Shi and J. Malik, “Normalized cuts and imagesegmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 22, No. 8, 2000.
• Maximization of modularity, M. E. J. Newman and M. Girvan, “Finding and evaluating
• community structure in networks”, Phys. Rev. E 69, 026113, (2004).
  
  

  
  Modularity can be calculated in igraph.

• Markov chain clustering (MCL)

Related URLs

• http://www.sixhat.net/finding-communities-in-networks-with-r-and-igraph.html