Abstract
The influx of biomedical measurement technologies continues to define a rapidly changing and growing landscape, multi-modal and uncertain in nature. The focus of the biomedical research community shifted from pure data generation to the development of methodologies for data analytics. Although many researchers continue to focus on approaches developed for analyzing single types of biological data, recent attempts have been made to utilize the availability of multiple heterogeneous data sets that contain various types of data and try to establish tools for data fusion and analysis in many bioinformatics applications. At the heart of this initiative is the attempt to consolidate the domain knowledge and experimental data sources in order to enhance our understanding of highly-specific conditions dependent on sensory data containing inherent error. This challenge refers to granularity: the specificity or mereology of alternate information sources may impact the final data fusion. In an earlier work, we employed data integration methods to analyze biological data obtained from protein interaction networks and gene expression data. We conducted a study to show that potential problems can arise from integrating or fusing data obtained at different granularity levels and highlight the importance of developing advanced data fusing techniques to integrate various types of biological data for analytical purposes. In this work, we explore the impact of granularity from a more formulized approach and show that granularity levels significantly impact the quality of knowledge extracted from the heterogeneous data sets. Further, we extend our previous results to study the relationship between granularity and knowledge extraction across multiple diseases, examining generalizability and estimating the utility of a similar methodology to reflect the impact of granularity levels.
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References
Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W., Pages, F., Trajanoski, Z., Galon, J.: ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25(8), 1091–1093 (2009)
Bittner, T., Smith, B.: A theory of granular partitions. Found. Geogr. Inf. Sci. 7, 124–125 (2003)
Bittner, T., Donnelly, M., Smith, B.: Individuals, universals, collections: on the foundational relations of ontology. In: Proceedings of the Third Conference on Formal Ontology in Information Systems, pp. 37–48, November 2004
Brazma, A.: Minimum information about a microarray experiment (MIAME)–successes, failures, challenges. Sci. World J. 9, 420–423 (2009)
Ceol, A., Aryamontri, A. C., Licata, L., Peluso, D., Briganti, L., Perfetto, L., Castagnoli, L., Cesareni, G.: MINT, the molecular interaction database: 2009 update. Nucleic Acids Res. (2009). doi:10.1093/nar/gkp983
Chatr-aryamontri, A., Breitkreutz, B.J., Heinicke, S., Boucher, L., Winter, A., Stark, C., Nixon, J., Ramage, L., Tyers, M.: The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41(D1), D816–D823 (2013)
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Santos, A., Tsafou, K., Kuhn, M., Bork, P., Jensen, L.J., von Mering, C.: STRING v9. 1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41(D1), D808-D815 (2013)
Greer, J.E., McCalla, G.I.: A computational framework for granularity and its application to educational diagnosis. In: IJCAI, pp. 477–482, August 1989
Halevy, A., Rajaraman, A., Ordille, J.: Data integration: the teenage years. In: Proceedings of the 32nd international Conference on Very Large Data Bases, pp. 9–16. VLDB Endowment, September 2006
Hobbs, J.R.: Granularity. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (1985)
Hobbs, J.R.: Sketch of an ontology underlying the way we talk about the world. Int. J. Hum. Comput. Stud. 43(5), 819–830 (1995)
Jiang, P., Singh, M.: SPICi: a fast clustering algorithm for large biological networks. Bioinformatics 26(8), 1105–1111 (2010)
Kerrien, S., Aranda, B., Breuza, L., Bridge, A., Broackes-Carter, F., Chen, C., Duesbury, M., Dumousseau, M., Feuermann, M., Hinz, U., Jandrasits, C., Jimenez, R.C., Khadake, J., Mahadevan, U., Masson, P., Pedruzzi, I., Pfeiffenberger, E., Porras, P., Raghunath, A., Roechert, B., Orchard, S., Hermjakob, H.: The IntAct molecular interaction database in 2012. Nucleic Acids Res. (2011). doi:10.1093/nar/gkr1088
Liu, Z., Cao, J., Gao, X., Zhou, Y., Wen, L., Yang, X., Xuebiao, Y., Ren, J., Xue, Y.: CPLA 1.0: an integrated database of protein lysine acetylation. Nucleic Acids Res. 39(suppl. 1), D1029–D1034 (2011)
McCalla, G., Greer, J., Barrie, B., Pospisil, P.: Granularity hierarchies. Comput. Math Appl. 23(2), 363–375 (1992)
Obayashi, T., Kinoshita, K.: Rank of correlation coefficient as a comparable measure for biological significance of gene coexpression. DNA Res. 16(5), 249–260 (2009)
Prasad, T.K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, B., Venugopal, A., Balakrishnan, L., Marimuthu, A., Banerjee, S., Somanathan, D.S., Sebastian, A., Rani, S., Ray, S., Harrys Kishore, C.J., Kanth, S., Ahmed, M., Kashyap, M.K., Mohmood, R., Ramachandra, Y.L., Krishna, V., Rahiman, B.A., Mohan, S., Ranganathan, P., Ramabadran, S., Chaerkady, R., Pandey, A., Pandey, A.: Human protein reference database—2009 update. Nucleic Acids Res. 37(suppl. 1), D767–D772 (2009)
Rector, A., Rogers, J., Bittner, T.: Granularity, scale and collectivity: when size does and does not matter. J. Biomed. Inform. 39(3), 333–349 (2006)
Salwinski, L., Miller, C.S., Smith, A.J., Pettit, F.K., Bowie, J.U., Eisenberg, D.: The database of interacting proteins: 2004 update. Nucleic Acids Res. 32(suppl. 1), D449–D451 (2004)
Słowiński, R., Greco, S., Matarazzo, B.: Rough-set-based decision support. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies, pp. 557–609. Springer, New York (2014)
Sun, B., Ma, W.: Multigranulation rough set theory over two universes. J. Intell. Fuzzy Syst. Appl. Eng. Technol. 28(3), 1251–1269 (2015)
Veres, D.V., Gyurkó, D.M., Thaler, B., Szalay, K. Z., Fazekas, D., Korcsmáros, T., Csermely, P.: ComPPI: a cellular compartment-specific database for protein–protein interaction network analysis. Nucleic Acids Res. (2014). doi:10.1093/nar/gku1007
Vogt, L., Grobe, P., Quast, B., Bartolomaeus, T.: Accommodating ontologies to biological reality–top-level categories of cumulative-constitutively organized material entities. PLoS ONE 7(1), e30004 (2012)
West, S., Ali, H.: On the impact of granularity in extracting knowledge from bioinformatics data. In: The 7th International Conference on Bioinformatics Models, Methods, and Algorithms (Bioinformatics 2016), Rome, Italy, 22–25 February 2016
White, F.E.: Data fusion lexicon. Joint Directors of Labs Washington DC(1991)
Zhang, B., Horvath, S.: A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4(1), 1128 (2005)
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West, S., Ali, H. (2017). Sensitivity Analysis of Granularity Levels in Complex Biological Networks. In: Fred, A., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2016. Communications in Computer and Information Science, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-319-54717-6_10
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