Download Graph preprocessing

Graph preprocessing
 Introduction
 Noise removal and data enhancement problem
 Noise removal and data enhancement on binary data
 Noise removal and data enhancement on graph data
 Noise removal and data enhancement tools
 Current research problems
 Future directions
Protein Function and Interaction Data
• Proteins usually interact with other proteins to perform their function(s)
• Interaction data provides a glimpse into the mechanisms underlying
biological processes
o Networks of pairwise protein-protein interactions
o Protein complexes
o Neighboring proteins in an interaction network tend to perform similar
functions
o Several computational approaches proposed for predicting protein
function from interaction networks [Pandey et al, 2006]
• A group of proteins occurring in many complexes may represent a
functional modules that consists of proteins involved in similar
biological processes
Problems with Available Interaction Data
(I)
• Noise: Spurious or false positive interactions
Hart et
al,2006
• Leads to significant fall in performance of protein function
prediction algorithms [Deng et al, 2003]
Problems with Available Interaction Data
(II)
• Incompleteness: Unavailability of a major fraction of
interactomes of major organisms
Hart et al, 2006
• Yeast: 50%, Human: 11%
• May delay the discovery of important knowledge
Pre-processing of Protein Interaction
Networks
• Overall Objective: Accurate inference of protein function
from interaction networks
• Complexity: Noise and incompleteness in interaction networks
adversely impact accuracy of functional inferences [Deng et al,
2003]
• Potential Approach: Pre-processing of interaction networks
Our Approach
• Transform graph G=(V,E,W) into G’=(V,E’,W’)
Input PPI
graph
Transformed PPI
graph where Pi
and Pj are
connected if
(Pi,Pj) is a
hyperclique
pattern
• Tries to meet three objectives:
– Addition of potentially biologically valid edges
– Removal of potentially noisy edges
– Assignment of weights to the resultant set of edges that
indicate their reliability
Sparsification to remove spurious edges
Common neighborbased transformation
# edges = 6490
Pruning to remove
spurious edges
# edges = 95739
# edges = 6874
Related Approaches: Neighborhood-based
Similarity
i
j
i
j
• Motivation: Two proteins sharing several common neighbors are likely to have a valid
interaction
• Probability (p-value) of having m common neighbors given degrees of the two proteins n1 and
n2, and size of the network N [Samanta et al, 2003]
• Handles the problem of high degree nodes
• # common neighbors or Jacquard similarity (m/(n1+n2-m)) [Brun et al, 2003]
• Min(fractions of common neighbors) = Min(m/n1, m/n2)
– Identical to pairwise h-confidence
References
[1] Hui Xiong, X. He, Chris Ding, Ya Zhang, Vipin Kumar, Stephen R. Holbrook, Identification of
Functional Modules in Protein Complexes via Hyperclique Pattern Discovery, in Proc. of the Pacific
Symposium on Biocomputing, (PSB 2005), 2005
[2] Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, Addison-Wesley
April 2005
[3] Jinze Liu, Susan Paulsen, Xing Xu, Wei Wang, Andrew Nobel, Jan Prins, Mining Approximate
Frequent Item sets in the Presence of Noise: Algorithms and Analysis, SIAM 2006
[4] Pang-Ning Tan, Vipin Kumar, and Jaideep Srivastava, Selecting the Right Interestingness Measure for
Association Patterns, Proc of the Eighth ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data
Mining (SIGKDD-2002)
[5] Hui Xiong, Pang-Ning Tan, and Vipin Kumar, Mining Strong Affinity Association Patterns in Data Sets
with Skewed Support Distribution, In Proc. of the Third IEEE International Conference on Data Mining
(ICDM 2003)
[6] Hui Xiong, Pang-Ning Tan, and Vipin Kumar, Hyperclique Pattern Discovery, Data Mining and
Knowledge Discovery Journal, accepted for publication as a regular paper, 2006
[7] A. Gavin et al. Functional organization of the yeast proteome by systematic analysis of protein
complexes, Nature, 415:141-147, 2002
[8] Matteo Pellegrini et al., Assigning protein functions by comparative genome analysis: Protein
phylogenetic profiles, Proc. Natl. Acad. Sci. USA Vol. 96, pp. 4285–4288, April 1999, Biochemistry