Abstract
When biological networks are considered, the extraction of interesting knowledge often involves subgraphs isomorphism check that is known to be NP-complete. For this reason, many approaches try to simplify the problem under consideration by considering structures simpler than graphs, such as trees or paths. Furthermore, the number of existing approximate techniques is notably greater than the number of exact methods. In this chapter, we provide an overview of three important problems defined on biological networks: network alignment, network clustering, and motifs extraction from biological networks. For each of these problems, we also describe some of the most important techniques proposed to approach them.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamcsek, B., et al.: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22(8), 1021–1023 (2006)
Amelio, A., Apostolico, A., Rombo, S.E.: Image compression by 2D motif basis. In: Data Compression Conference (DCC’11), pp. 153–162 (2011)
Apostolico, A., et al.: Finding 3d motifs in ribosomal rna structures. Nucl. Acids Res. (2008)
Apostolico, A., Parida, L.: Incremental paradigms of motif discovery. J. Comput. Biol. 11(1), 15–25 (2004)
Apostolico, A., Bock, M.E., Lonardi, S.: Monotony of surprise and large-scale quest for unusual words. J. Comput. Biol. 10(2/3), 283–311 (2003)
Apostolico, A., Parida, L., Rombo, S.E.: Motif patterns in 2D. Theor. Comput. Sci. 390(1), 40–55 (2008)
Bader, G., Hogue, H.: An automated method for finding molecular complexes in large protein-protein interaction networks. BMC Bioinform. 4(2) (2003)
Bandyopadhyay, S., Sharan, R., Ideker, T.: Systematic identification of functional orthologs based on protein network comparison. Genome Res. 16(3), 428–435 (2006)
Berg, J., Lassig, M.: Local graph alignment and motif search in biological networks. Proc. Natl. Acad. Sci. USA 101(41), 14689–14694 (2004)
Bruckner, S., Hüffner, F., Karp, R.M., Shamir, R., Sharan, R.: Torque: topology-free querying of protein interaction networks. Nucl. Acids Res. 37(Web-Server-Issue), 106–108 (2009)
Cheng, C.Y., Huang, C.Y., Sun, C.T.: Mining bridge and brick motifs from complex biological networks for functionally and statistically significant discovery. IEEE Trans. Syst. Man Cybern. Part B 38(1), 17–24 (2008)
Ciriello, G., Guerra, C.: A review on models and algorithms for motif discovery in protein-protein interaction network. Brief. Funct. Genomics Proteomics (2008)
Ciriello, G., Mina, M., Guzzi, P.H., Cannataro, M., Guerra, C.: AlignNemo: A local network alignment method to integrate homology and topology. PLOS One 7(6), e38,107 (2012)
Denielou, Y.P., Boyer, F., Viari, A., Sagot, M.F.: Multiple alignment of biological networks: a flexible approach. In: CPM’09 (2009)
Derenyi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Phys. Rev. Lett. 94(16), 160–202 (2005)
Dost, B., et al.: Qnet: a tool for querying protein interaction networks. In: RECOMB’07, pp. 1–15 (2007)
Enright, A., Dongen, S., Ouzounis, C.: An efficient algorithm for large-scale detection of protein families. Nucl. Acids Res. 30(7), 1575–84 (2002)
Erdos, P., Renyi, A.: On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17–61 (1960)
Ferraro, N., Palopoli, L., Panni, S., Rombo, S.E.: Master-slave biological network alignment. In: 6th International symposium on Bioinformatics Research and Applications (ISBRA 2010), pp. 215–229 (2010)
Ferraro, N., et al.: Asymmetric comparison and querying of biological networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 8, 876–889 (2011)
Ferro, A., et al.: Netmatch: a cytoscape plugin for searching biological networks. Bioinformatics (2007)
Fionda, V., Palopoli, L., Panni, S., Rombo, S.E.: Protein-protein interaction network querying by a “focus and zoom” approach. In: BIRD’08, pp. 331–346 (2008)
Fionda, V., Panni, S., Palopoli, L., Rombo, S.E.: A technique to search functional similarities in PPI networks. Int. J. Data Mining Bioinform. (To appear)
Flannick, J., Novak, A., Graemlin, S., et al.: General and robust alignment of multiple large interaction networks. Genome Res. 16(9), 1169–1181 (2006)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
Furfaro, A., Groccia, M.C., Rombo, S.E.: Image classification based on 2D feature motifs. In: Flexible Query Answering Systems (FQAS 2013) (2013)
Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, New York (1979)
Grossi, R., Pisanti, N., Crochemore, M., Sagot, M.F.: Bases of motifs for generating repeated patterns with wild cards. IEEE/ACM Trans. Comput. Biol. Bioinform. 2(3), 159–177 (2000)
Jancura, P., et al.: A methodology for detecting the orthology signal in a PPI network at a functional complex level. BMC Bioinform. (2011)
Kalaev, M., Bafna, V., Sharan, R.: Fast and accurate alignment of multiple protein networks. In: RECOMB’08 (2008)
Kelley, B., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B.R., Ideker, T.: Pathblast: a tool for alignment of protein interaction networks. Nucl. Acid Res. 32, W83–W88 (2004)
Kim, W., Li, M., Wang, J., Pan, Y.: Biological network motif detection and evaluation. BMC Syst. Biol. 5(Suppl 3), S5 (2011)
King, A.D., Pržulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20(17), 3013–3020 (2004)
Klau, G.W.: A new graph-based method for pairwise global network alignment. BMC Bioinform. 10(Suppl. 1), S59 (2009)
Koyuturk, M., Kim, Y., Topkara, U., Subramaniam, S., Szpankowski, W., Grama, A.: Pairwise alignment of protein interaction networks. J. Comput. Biol. 13(2), 182–199 (2006)
Kuchaiev, O., Przulj, N.: Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 27(10), 1390–1396 (2011)
Lacroix, V., Fernandes, C.G., Sagot, M.F.: Motif search in graphs: application to metabolic networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 3(4), 360–368 (2006)
Leskovec, J., Lang, K., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Proceedings of the International World Wide Web Conference (WWW), pp. 631–640 (2010)
Liao, C.S., et al.: Isorankn: spectral methods for global alignment of multiple protein networks. Bioinformatics 25, i253–i258 (2009)
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE Trans. Comput. Biol. Bioinform. 1(1), 24–45 (2004)
Mangan, S., Alon, U.: Structure and function of the feed-forward loop network motif. Proc. Natl. Acad. Sci. USA 100(21), 11980–11985 (2003)
Mangan, S., Itzkovitz, S., Zaslaver, A., Alon, U.: The incoherent feed-forward loop accelerates the response-time of the gal system of escherichia coli. J. Mol. Biol. 356(5), 1073–1081 (2005)
Milo, R., et al.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)
Mongiov, M., Sharan, R.: Global alignment of protein-protein interaction networks. In: Mamitsuka, H., DeLisi, C. Kanehisa, M. (eds.) Data Mining for Systems Biology, Methods in Molecular Biology, vol. 939, pp. 21–34. Humana Press (2013)
Neyshabur, B., Khadem1, A., Hashemifar, S., Arab, S.S.: NETAL: a new graph-based method for global alignment of protein?protein interaction networks. Bioinformatics 29(13), 11,654–1662 (2013)
Palla, G., et al.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Parida, L.: Pattern Discovery in Bioinformatics. Theory and Algorithms. Chapman and HAll/CRC (2008)
Parida, L.: Discovering topological motifs using a compact notation. J. Comput. Biol. 14(3), 46–69 (2007)
Park, Y., Song, M.: A genetic algorithm for clustering problems. In: Proceedings of 3rd Annual Conference on Genetic Algorithms, pp. 2–9 (1989)
Pinter, R., et al.: Alignment of metabolic pathways. Bioinformatics 21(16), 3401–3408 (2005)
Pizzuti, C., Rombo, S.E.: Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 193–200 (2012)
Pizzuti, C., Rombo, S.E.: Multi-functional protein clustering in PPI networks. In: Proceedings of the 2nd International Conference on Bioinformatics Research and Development (BIRD), pp. 318–330 (2008)
Pizzuti, C., Rombo, S.E.: Pincoc: a co-clustering based approach to analyze protein-protein interaction networks. In: Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning, pp. 821–830 (2007)
Pizzuti, C., Rombo, S.E.: Restricted neighborhood search clustering revisited: an evolutionary computation perspective. In: Proceedings of the 8th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB), pp. 59–68 (2013)
Pizzuti, C., Rombo, S.E.: A coclustering approach for mining large protein-protein interaction networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(3), 717–730 (2012)
Rombo, S.E.: Optimal extraction of motif patterns in 2D. Inf. Process. Lett. 109(17), 1015–1020 (2009)
Rombo, S.E.: Extracting string motif bases for quorum higher than two. Theor. Comput. Sci. 460, 94–103 (2012)
Ruan, J., Zhang, W.: Identifying network communities with a high resolution. Phys. Rev. E 77(1) (2008)
Sharan, R., et al.: From the cover: conserved patterns of protein interaction in multiple species. Proc. Natl. Acad. Sci. USA 102(6), 1974–1979 (2005)
Sharan, R., Ideker, T.: Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24(4), 427–433 (2006)
Shen-Orr, S.S., Milo, R., Mangan, S., Alon, U.: Network motifs in the trascriptional regulation network of escherichia coli. Nature 31, 64–68 (2002)
Shih, Y.K., Parthasarathy, S.: Scalable global alignment for multiple biological networks. BMC Bioinform. 13(Suppl 3), S11 (2012)
Shlomi, T., et al.: Qpath: a method for querying pathways in a protein-protein interaction network. BMC Bioinform. 7 (2006)
Singh, R., Xu, J., Berger, B.: Global alignment of multiple protein interaction networks. In: PSB’08 (2008)
Singh, R., Xu, J., Berger, B.: Pairwise global alignment of protein interaction networks by matching neighborhood topology. In: Research in Computational Molecular Biology (RECOMB 2007), pp. 16–31 (2007)
Van Dongen, S.: Graph clustering via a discrete uncoupling process. SIAM J. Matrix Anal. Appl. 30(1), 121–141 (2008)
Wu, X., Liu, Q., Jiang, R.: Align human interactome with phenome to identify causative genes and networks underlying disease families. Bioinformatics 25(1), 98–104 (2009)
Yang, Q., Sze, S.H.: Saga: a subgraph matching tool for biological graphs. J. Comput. Biol. 14(1), 56–67 (2007)
Yeger-Lotem, E., et al.: Network motifs in integrated cellular networks of transcriptionregulation and proteinprotein interaction. Proc. Natl. Acad. Sci. USA 101(16), 5934–5939 (2004)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Fassetti, F., Rombo, S.E., Serrao, C. (2017). Problems and Techniques. In: Discriminative Pattern Discovery on Biological Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-63477-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-63477-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63476-0
Online ISBN: 978-3-319-63477-7
eBook Packages: Computer ScienceComputer Science (R0)