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Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Parallel computing architectures are proven to significantly shorten computation time for different clustering algorithms. Nonetheless, some characteristics of the architecture limit the application of graphics processing units (GPUs) for biclustering task, whose function is to find focal similarities within the data. This might be one of the reasons why there have not been many biclustering algorithms proposed so far. In this article, we verify if there is any potential for application of complex biclustering calculations (CPU+GPU). We introduce minimax with Pearson correlation – a complex biclustering method. The algorithm utilizes Pearson’s correlation to determine similarity between rows of input matrix. We present two implementations of the algorithm, sequential and parallel, which are dedicated for heterogeneous environments. We verify the weak scaling efficiency to assess if a heterogeneous architecture may successfully shorten heavy biclustering computation time.
Rocznik
Strony
243--248
Opis fizyczny
Bibliogr. 20 poz., wykr.
Twórcy
  • Faculty of Electrical Engineering, Department of Automatics and Bioengineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza Av. 30, 30-059 Cracow, Poland
autor
  • Faculty of Computer Science, Department of Computer Science, Electronics, and Telecommunications, AGH University of Science and Technology, Cracow, Poland
Bibliografia
  • 1. Cheng Y, Church G. Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology 2000;8:93–103.
  • 2. Eren K, Deveci M, Küçüktunç O, Çatalyürek Ü. A comparative analysis of biclustering algorithms for gene expression data. Brief Bioinform 2013;14:279–92.
  • 3. Madeira S, Oliveira A. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform 2004;1:24–45.
  • 4. Prelić A, Bleuler S, Zimmermann P, Wille A, Bühlmann P, Gruissem W, et al. A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 2006;22:1122–9.
  • 5. Bisson G, Hussain F. Chi-sim: a new similarity measure for the coclustering task. In: Seventh International Conference on Machine Learning and Applications, ICMLA ‘08, December 2008:211–7.
  • 6. Busygin S, Prokopyev O, Pardalos PM. Biclustering in data mining. Comput Oper Res 2008;35:2964–87.
  • 7. de Franca F, Coelho G, Zuben FV. Predicting missing values with biclustering: a coherence-based approach. Pattern Recog 2013;46:1255–66.
  • 8. Cristovao F, Madeira S. Parallel e-ccc-biclustering: mining approximate temporal patterns in gene expression time series using parallel biclustering. In: Rocha MP, Luscombe N, Fdez-Riverola F, Rodríguez JM, editors. 6th International Conference on Practical Applications of Computational Biology and Bioinformatics, Adv Intell Soft Comput 2012;154:21–31. Berlin, Heidelberg: Springer-Verlag. http://dx.doi.org/10.1007/978-3-642-28839-5_3.
  • 9. Liu B, Xin Y, Cheung RC, Yan H. GPU-based biclustering for microarray data analysis in neurocomputing. Neurocomputing 2014;134:239–46.
  • 10. Lo A, Liu B, Cheung R. GPU-based biclustering for neural information processing. In: Huang T, Zeng Z, Li C, Leung C, editors. Neural information processing, Lecture Notes Comput Sci 2012;7667:134–41. Berlin, Heidelberg: Springer-Verlag. http://dx.doi.org/10.1007/978-3-642-34500-5_17.
  • 11. Mejia-Roa E, Garcia C, Gomez JI, Prieto M, Tirado F, Nogales R, et al. Biclustering and classification analysis in gene expression using nonnegative matrix factorization on multi-GPU systems. In: 11th International Conference on Intelligent Systems Design and Applications (ISDA). Cordoba, Spain: IEEE 2011:882–7.
  • 12. Aguilar-Ruiz J. Shifting and scaling patterns from gene expression data. Bioinformatics 2005;21:3840–5.
  • 13. Bozdağ D, Parvin JD, Catalyurek UV. A biclustering method to discover co-regulated genes using diverse gene expression datasets. In: Proceedings of the 1st International Conference on Bioinformatics and Computational Biology, BICoB ‘09. Berlin, Heidelberg: Springer-Verlag, 2009:151–63. http://dx.doi.org/10.1007/978-3-642-00727-9_16.
  • 14. Ben-Dor A, Chor B, Karp R, Yakhini Z. Discovering local structure in gene expression data: the order-preserving submatrix problem. In: Proceedings of the Sixth Annual International Conference on Computational Biology, RECOMB ‘02, ACM, New York, NY, USA, 2002:49–57. http://doi.acm.org/10.1145/565196.565203.
  • 15. Li G, Ma Q, Tang H, Paterson A, Xu Y. QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucl Acids Res 2009;37:e101.
  • 16. Orzechowski P, Boryczko K. Effective biclustering on GPU – capabilities and constraints. Prz Elektrotechniczn 2015;1:133–6.
  • 17. NVIDIA Corporation: CUDA C Programming Guide 2014, pG-02829-001_v6.0.
  • 18. Lazzeroni L, Owen A. Plaid models for gene expression data. Stat Sin 2002;12:61–86.
  • 19. Murali T, Kasif S. Extracting conserved gene expression motifs from gene expression data. In: Proceeding of the Pacific Symposium on Biocomputing, vol. 3, 2003:77–88.
  • 20. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995;57:289–300.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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