Skip to main content

Multi-faceted Functional Decomposition

  • Chapter
  • First Online:
Summarizing Biological Networks

Part of the book series: Computational Biology ((COBO,volume 24))

  • 846 Accesses

Abstract

In this chapter, we present a ppi decomposition algorithm called facetsĀ [1] in order to make sense of the deluge of interaction data using go annotation data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We use the term orthogonal to describe the idea of distinctive clusters, rather than its precise mathematical meaning.

References

  1. B.-S. Seah, S.S. Bhowmick, C.F. Dewey, FACETS: multi-faceted functional decomposition of protein interaction networks. Bioinformatics (Oxford, England) 28, 2624ā€“2631 (2012)

    Google ScholarĀ 

  2. G.D. Bader, C.W.V. Hogue, An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 4, 2 (2003)

    ArticleĀ  Google ScholarĀ 

  3. N.J. Krogan, G. Cagney, H. Yu, G. Zhong, X. Guo, A. Ignatchenko, J. Li, S. Pu, N. Datta, A.P. Tikuisis, T. Punna, J.M. PeregrĆ­n-Alvarez, M. Shales, X. Zhang, M. Davey, M.D. Robinson, A. Paccanaro, J.E. Bray, A. Sheung, B. Beattie, D.P. Richards, V. Canadien, A. Lalev, F. Mena, P. Wong, A. Starostine, M.M. Canete, J. Vlasblom, S. Wu, C. Orsi, S.R. Collins, S. Chandran, R. Haw, J.J. Rilstone, K. Gandi, N.J. Thompson, G. Musso, P. St Onge, S. Ghanny, M.H.Y. Lam, G. Butland, A.M. Altaf-Ul, S. Kanaya, A. Shilatifard, E. Oā€™Shea, J.S. Weissman, C.J. Ingles, T.R. Hughes, J. Parkinson, M. Gerstein, S.J. Wodak, A. Emili, J.F. Greenblatt, Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637ā€“643 (2006). Mar

    Google ScholarĀ 

  4. B.-S. Seah, S.S. Bhowmick, C.F. Dewey, H. Yu, FUSE: a profit maximization approach for functional summarization of biological networks. BMC Bioinform. 13(Suppl 3), S10 (2012). Jan

    Google ScholarĀ 

  5. Z.Ā Qi, I.Ā Davidson, A principled and flexible framework for finding alternative clusterings, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ā€™09, (ACM Press, New York, 2009), p.Ā 717

    Google ScholarĀ 

  6. D.Ā Niu, J.G.Ā Dy, M.Ā I. Jordan, Multiple non-redundant spectral clustering views, in Proceeding of the 27th International Conference on Machine Learning - ICML ā€™10, Haifa, Israel (2010)

    Google ScholarĀ 

  7. Y.Ā Cui, X.Z. Fern, J.G. Dy, Non-redundant Multi-view Clustering via Orthogonalization, vol.Ā 3, (IEEE, 2007)

    Google ScholarĀ 

  8. C.C. Kiri Wagstaff, Clustering with instance-level constraints, in Proceedings of the Seventeenth International Conference on Machine Learning (2000)

    Google ScholarĀ 

  9. N.N. Rich Caruana , Mohamed Elhawary, Meta clustering, in IEEE International Conference on Data Mining (2006)

    Google ScholarĀ 

  10. G. Agarwal, D. Kempe, Modularity-maximizing graph communities via mathematical programming. Eur. Phys. J. B 66, 409ā€“418 (2008). Nov

    Google ScholarĀ 

  11. C. Massen, J. Doye, Identifying communities within energy landscapes. Phys. Rev. E 71, 046101 (2005). Apr

    Google ScholarĀ 

  12. C.Ā Kingsford, S.Ā Navlakha, Exploring biological network dynamics with ensembles of graph partitions, in Pacific Symposium Biocomputer (2010), pp.Ā 166ā€“77

    Google ScholarĀ 

  13. S. Navlakha, J. White, N. Nagarajan, M. Pop, C. Kingsford, Finding biologically accurate clusterings in hierarchical tree decompositions using the variation of information. J. Comput. Biol. J. Comput. Mol. cell Biol. 17, 503ā€“516 (2010). Mar

    Google ScholarĀ 

  14. A. Jagota, Approximating maximum clique with a Hopfield network. IEEE Trans. Neural Netw. Publ. IEEE Neural Netw. Counc. 6, 724ā€“735 (1995). Jan

    Google ScholarĀ 

  15. Y.Ā Botton, L. Bengio, Convergence properties of the k-means algorithms, in In Advances in Neural Information Processing Systems 7 (1994)

    Google ScholarĀ 

  16. S. Kerrien, Y. Alam-Faruque, B. Aranda, I. Bancarz, A. Bridge, C. Derow, E. Dimmer, M. Feuermann, A. Friedrichsen, R. Huntley, C. Kohler, J. Khadake, C. Leroy, A. Liban, C. Lieftink, L. Montecchi-Palazzi, S. Orchard, J. Risse, K. Robbe, B. Roechert, D. Thorneycroft, Y. Zhang, R. Apweiler, H. Hermjakob, IntAct-open source resource for molecular interaction data. Nucl. Acids Res. 35, D561ā€“D565 (2007). Jan

    Google ScholarĀ 

  17. A.Ā Ben-Hur, A.Ā Elisseeff, I.Ā Guyon, A stability based method for discovering structure in clustered data, in Biocomputing 2002 - Proceedings of the Pacific Symposium, (World Scientific Publishing Co. Pte. Ltd., Singapore, 2001), pp.Ā 6ā€“17

    Google ScholarĀ 

  18. H.W. Mewes, D. Frishman, U. GĆ¼ldener, G. Mannhaupt, K. Mayer, M. Mokrejs, B. Morgenstern, M. MĆ¼nsterkƶtter, S. Rudd, B. Weil, MIPS: a database for genomes and protein sequences. Nucl. Acids Res. 30, 31ā€“34 (2002). Jan

    Google ScholarĀ 

  19. C.G. Rivera, R. Vakil, J.S. Bader, NeMo: Network Module identification in Cytoscape. BMC Bioinform. 11(Suppl 1), S61 (2010). Jan

    Google ScholarĀ 

  20. E.I. Boyle, S. Weng, J. Gollub, H. Jin, D. Botstein, J.M. Cherry, G. Sherlock, GO::TermFinderā€“open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics (Oxford, England) 20, 3710ā€“3715 (2004)

    Google ScholarĀ 

  21. G.T. Hart, A.K. Ramani, E.M. Marcotte, How complete are current yeast and human protein-interaction networks? Gen. Biol. 7, 120 (2006). Jan

    Google ScholarĀ 

  22. P.K. Chan, M.D.F. Schlag, J.Y. Zien, Spectral K-way Ratio-cut Partitioning and Clustering (ACM Press, New York, 1993)

    BookĀ  Google ScholarĀ 

  23. N. Mizushima, B. Levine, A.M. Cuervo, D.J. Klionsky, Autophagy fights disease through cellular self-digestion. Nature 451, 1069ā€“1075 (2008). Feb

    Google ScholarĀ 

  24. C. Behrends, M.E. Sowa, S.P. Gygi, J.W. Harper, Network organization of the human autophagy system. Nature 466, 68ā€“76 (2010). July

    Google ScholarĀ 

  25. I. Novak, V. Kirkin, D.G. McEwan, J. Zhang, P. Wild, A. Rozenknop, V. Rogov, F. Lƶhr, D. Popovic, A. Occhipinti, A.S. Reichert, J. Terzic, V. Dƶtsch, P.A. Ney, I. Dikic, Nix is a selective autophagy receptor for mitochondrial clearance. EMBO Rep. 11, 45ā€“51 (2010). Jan

    Google ScholarĀ 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sourav S. Bhowmick .

Rights and permissions

Reprints and permissions

Copyright information

Ā© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Bhowmick, S.S., Seah, BS. (2017). Multi-faceted Functional Decomposition. In: Summarizing Biological Networks. Computational Biology, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-54621-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54621-6_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54620-9

  • Online ISBN: 978-3-319-54621-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics