Graph preprocessing

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Graph preprocessing
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Introduction
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Introduction
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Introduction
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Protein Function and Interaction Data
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Problems with Available Interaction Data (I)

• Noise Spurious or false positive interactions
• Leads to significant fall in performance of
  protein function prediction algorithms Deng et
  al, 2003

Hart et al,2006
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Problems with Available Interaction Data (II)

• Incompleteness Unavailability of a major
  fraction of interactomes of major organisms
• Yeast 50, Human 11
• May delay the discovery of important knowledge

Hart et al, 2006
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Introduction
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Introduction
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Data cleaning techniques at data collection stage
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Purpose of data cleaning techniques data analysis
view point
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Purpose of data cleaning techniques data analysis
view point
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Data cleaning techniques at the data analysis
stage
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Neighbourhood-based approaches for graph
transformation
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References (I)

• Pandey et al, 2006 Gaurav Pandey, Vipin Kumar
  and Michael Steinbach, Computational Approaches
  for Protein Function Prediction A Survey, TR
  06-028, Department of Computer Science and
  Engineering, University of Minnesota, Twin Cities
• Pandey et al, 2007 G. Pandey, M. Steinbach, R.
  Gupta, T. Garg and V. Kumar, Association
  analysis-based transformations for protein
  interaction networks a function prediction case
  study. KDD 2007 540-549
• Xiong et al, 2005 XIONG, H., HE, X., DING, C.,
  ZHANG, Y., KUMAR, V., AND HOLBROOK, S. R. 2005.
  Identification of functional modules in protein
  complexes via hyperclique pattern discovery. In
  Proc. Pacific Symposium on Biocomputing (PSB).
  221232.
• Xiong et al, 2006a XIONG, H., TAN, P.-N., AND
  KUMAR, V. 2003. Hyperclique Pattern Discovery,
  Data Mining and Knowledge Discovery,
  13(2)219-242
• Xiong et al, 2006b XIONG, H., PANDEY, G.,
  STEINBACH, M., AND KUMAR, V. 2006, Enhancing Data
  Analysis with Noise Removal, IEEE TKDE,
  18(3)304-319
• Xiong et al, 2006c Hui Xiong, Michael
  Steinbach, and Vipin Kumar, Privacy Leakage in
  Multi-relational Databases A Semi-supervised
  Learning Perspective, VLDB Journal Special Issue
  on Privacy Preserving Data Management , Vol. 15,
  No. 4, pp. 388-402, November, 2006
• Xiong et al, 2004 Hui Xiong, Michael Steinbach,
  Pang-Ning Tan and Vipin Kumar, HICAP
  Hierarchical Clustering with Pattern
  Preservation, SIAM Data Mining 2004
• Tan et al, 2005 TAN, P.-N., STEINBACH, M., AND
  KUMAR, V. 2005. Introduction to Data Mining.
  Addison-Wesley.
• Nabieva et al, 2005 NABIEVA, E., JIM, K.,
  AGARWAL, A., CHAZELLE, B., AND SINGH, M. 2005.
  Whole-proteome prediction of protein function via
  graph-theoretic analysis of interaction maps.
  Bioinformatics 21, Suppl. 1, i1i9.
• Deng et al, 2003 DENG, M., SUN, F., AND CHEN,
  T. 2003. Assessment of the reliability of
  proteinprotein interactions and protein function
  prediction. In Pac Symp Biocomputing. 140151.
• Gavin et al, 2002 A. Gavin et al. Functional
  organization of the yeast proteome by systematic
  analysis of protein complexes, Nature, 
  415141-147, 2002
• Hart et al, 2006 G Traver Hart, Arun K Ramani
  and Edward M Marcotte, How complete are current
  yeast and human protein-interaction networks,
  Genome Biology, 7120, 2006