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Tytuł artykułu

On Boolean Representation of Continuous Data Biclustering

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Biclustering is considered as the method of finding two-dimensional subgroups in a matrix of scalars. The paper introduces a new approach to biclustering continuous matrices on the basis of boolean function analysis. We draw the strong relation between inclusion-maximal (maximal with respect to inclusion) biclusters of the assumed maximal difference between the data in a bicluster and prime implicants of a boolean function describing the data. These biclusters are called similarity biclusters. In the opposition to them, a new notion of dissimilarity biclusters was also introduced in the paper.
Wydawca
Rocznik
Strony
193--217
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
  • Institute of Informatics, University of Warsaw, ul. Banacha, 02-097 Warsaw, Poland
Bibliografia
  • [1] Michalak M, Ślęzak D. Boolean Representation for Exact Biclustering. Fundamenta Informaticae, 2018. 161(3):275-297. doi:10.3233/FI-2018-1703.
  • [2] Hartigan JA. Direct Clustering of a Data Matrix. Journal of the American Statistical Association, 1972. 67(337):123-129. doi:10.2307/2284710.
  • [3] Chagoyen M, Carmona-Saez P, Shatkay H, Carazo JM, Pascual-Montano A. Discovering Semantic Features in the Literature: A Foundation for Building Functional Associations. BMC Bioinformatics, 2006. 7(41). URL https://doi.org/10.1186/1471-2105-7-41.
  • [4] Orzechowski P, Pańszczyk A, Huang X, Moore JH. RUNIBIC: A Bioconductor Package for Parallel Row-based Biclustering of Gene Expression Data. Bioinformatics, 2018. 34(24):4302-4304. doi:10.1093/bioinformatics/bty512.
  • [5] Latkowski R. On Decomposition for Incomplete Data. Fundamenta Informaticae, 2003. 54:1-16.
  • [6] Busygin S, Prokopyev O, Pardalos PM. Biclustering in Data Mining. Computers & Operations Research, 2008. 35(9):2964-2987. URL https://doi.org/10.1016/j.cor.2007.01.005.
  • [7] Cheng Y, Church G. Biclustering of Expression Data. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 2000. 8:93-103. PMID:10977070.
  • [8] Yang J, Wang H, Wang W, Yu P. Enhanced Biclustering on Expression Data. In: Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengineering. 2003 pp. 321-327. doi:10.1109/BIBE.2003.1188969.
  • [9] Murali TM, Kasif S. Extracting Conserved Gene Expression Motifs from Gene Expression Data. In: Proceedings of Pacific Symposium on Biocomputing. 2003 pp. 77-88. PMID:12603019.
  • [10] Prelić A, Bleuler S, Zimmermann P, Wille A, Bühlmann P, Gruissem W, Hennig L, Thiele L, Zitzler E. A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data. Bioinformatics, 2006. 22(9):1122-1129. doi:10.1093/bioinformatics/btl060.
  • [11] Hochreiter S, Bodenhofer U, Heusel M, Mayr A, Mitterecker A, Kasim A, Khamiakova T, Van Sanden S, Lin D, Talloen W, Bijnens L, Ghlmann HWH, Shkedy Z, Clevert DA. FABIA: Factor Analysis for Bicluster Acquisition. Bioinformatics, 2010. 26(12):1520-1527. doi:10.1093/bioinformatics/btq227.
  • [12] Kasim A, Shkedy Z, Kaiser S, Hochreiter S, Talloen W. Applied Biclustering Methods for Big and High Dimensional Data using R. CRC Press, Taylor & Francis Group, 2016. ISBN:14822082379781482208238.
  • [13] Ignatov DI, Watson BW. Towards a Unified Taxonomy of Biclustering Methods. In: Russian and South African Workshop on Knowledge Discovery Techniques Based on Formal Concept Analysis, volume 1522. 2016 pp. 23-39.
  • [14] Tanay A, Sharan R, Shamir R. Discovering Statistically Significant Biclusters in Gene Expression Data. Bioinformatics, 2002. 18(suppl 1):S136-S144. URL https://doi.org/10.1093/bioinformatics/18.suppl_1.S136.
  • [15] Brown F. Boolean Reasoning. The Logic of Boolean Equations. Springer Science + Business Media, 1990. doi:10.1007/978-1-4757-2078-5.
  • [16] Jabbour S, Sais L, Salhi Y. The Top-k Frequent Closed Itemset Mining Using Top-k SAT Problem. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2013 pp. 403-418. doi:10.1007/978-3-642-40994-3_26.
  • [17] Michalak M, Stawarz M. Generating and Postprocessing of Biclusters from Discrete Value Matrices. In: Proceedings of the Third International Conference Computational Collective Intelligence. 2011 pp.103-112. doi:10.1007/978-3-642-23935-9_10.
  • [18] Stawarz M, Michalak M. eBi - the Algorithm for Exact Biclustering. In: Proceedings of the 11th International Conference on Artificial Intelligence and Soft Computing. 2012 pp. 327-334. doi:10.1007/978-3-642-29350-4_39.
  • [19] Michalak M, Lachor M, Polański A. HiBi - the Algorithm of Biclustering the Discrete Data. In: Proceedings of the 13th International Conference on Artificial Intelligence and Soft Computing. 2014 pp.760-771. doi:10.1007/978-3-319-07176-3_66.
  • [20] Michalak M, Stawarz M. HRoBi - the Algorithm for Hierarchical Rough Biclustering. In: Proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing. 2013 pp. 194-205. doi:10.1007/978-3-642-38610-7_19.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5dd80741-1e58-49f0-bd6a-6c49500910fa
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