HPC Tools to Deal with Microarray Data

  • Jorge González-DomínguezEmail author
  • Roberto R. Expósito
Part of the Methods in Molecular Biology book series (MIMB, volume 1986)


Parallel and high performance computing is continuously gaining attention in the last years as a means to accelerate several kind of computationally expensive applications. This chapter is a review of different research works and publicly available tools whose target is the acceleration of microarray data analysis, thanks to exploiting high performance computing systems.

Key words

Microarray data High performance computing Parallel computing 


  1. 1.
    Abdelrahman TS (2016) Accelerating K-means clustering on a tightly-coupled processor-FPGA heterogeneous system. In: 2016 IEEE international conference on application-specific systems, architectures and processors (ASAP), pp 176–181Google Scholar
  2. 2.
    Abduallah Y, Turki T, Byron K, Du Z, Cervantes-Cervantes M, Wang JTL (2017) MapReduce algorithms for inferring gene regulatory networks from time-series microarray data using an information-theoretic approach. BioMed Res Int. Scholar
  3. 3.
    Agapito G, Cannataro M, Guzzi PH, Marozzo F, Talia D, Trunfio P (2013) Cloud4SNP: distributed analysis of SNP microarray data on the cloud. In: 2013 International conference on bioinformatics, computational biology and biomedical informatics (BCB), p 468Google Scholar
  4. 4.
    Alborzi SZ, Maduranga DAK, Fan R, Rajapakse JC, Zheng J (2014) CUDAGRN: parallel speedup of inferring large gene regulatory networks from expression data using random forest. In: 2014 IAPR international conference on pattern recognition in bioinformatics (PRIB), pp 85–97Google Scholar
  5. 5.
    ARACNe-AP: network reverse engineering through AP inference of mutual information (2018). Last accessed March 2018
  6. 6.
    Asadi NB, Fletcher CW, Gibeling G, Glass EN, Sachs K, Burke D, Zhou Z, Wawrzynek J, Wong WH, Nolan GP (2010) Paralearn: a massively parallel, scalable system for learning interaction networks on FPGAs. In: 2010 ACM international conference on supercomputing (SC), pp 83–94Google Scholar
  7. 7.
    Belean B, Borda M, Le Gal B, Terebes R (2012) FPGA based system for automatic cDNA microarray image processing. Comput Med Imaging Graph 36(5):419–429PubMedCrossRefGoogle Scholar
  8. 8.
    Benso A, Di Carlo S, Politano G, Savino A (2010) GPU acceleration for statistical gene classification. In: 2010 IEEE international conference on automation quality and testing robotics (AQTR), vol 2, pp 1–6Google Scholar
  9. 9.
    Borelli FF, de Camargo RY, Martins DC, Rozante LCS (2013) Gene regulatory networks inference using a multi-GPU exhaustive search algorithm. BMC Bioinformatics 14(18):S5PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Buck I, Foley T, Horn D, Sugerman J, Fatahalian K, Houston M et al (2004) Brook for GPUs: stream computing on graphics hardware. ACM Trans Graph 23(3):777–786CrossRefGoogle Scholar
  11. 11.
    Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur Gener Comput Syst 25(6):599–616CrossRefGoogle Scholar
  12. 12.
    Canilho J, Véstias M, Neto H (2016) Multi-core for K-means clustering on FPGA. In: 2016 International conference on field programmable logic and applications (FPL), pp 1–4Google Scholar
  13. 13.
    Carastan-Santos D, de Camargo RY, Martins DC, Song SW, Rozante LCS, Borelli FF (2015) A multi-GPU hitting set algorithm for GRNs inference. In: 2015 IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid), pp 313–322Google Scholar
  14. 14.
    Carbone P, Katsifodimos A, Ewen S, Markl V, Haridi S, Tzoumas K (2015) Apache Flink: stream and batch processing in a single engine. Bull IEEE Comput Soc Tech Comm Data Eng 38(4):28–38Google Scholar
  15. 15.
    CFMDS (CUDA-based fast multidimensional scaling) (2018). Last accessed March 2018
  16. 16.
    Chen GK, Guo Y (2013) Discovering epistasis in large scale genetic association studies by exploiting graphics cards. Front Genet 4:266PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Chikkagoudar S, Wang K, Li M (2011) GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores. BMC Res Notes 4(1):158PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Chockalingam SP, Aluru M, Aluru S (2015) Information theory based genome-scale gene networks construction using mapreduce. In: 2015 IEEE international conference on high performance computing (HiPC), pp 464–473Google Scholar
  19. 19.
    Choi Y-M, So HK-H (2014) Map-reduce processing of K-means algorithm with FPGA-accelerated computer cluster. In: 2014 IEEE international conference on application-specific systems, architectures and processors (ASAP), pp 9–16Google Scholar
  20. 20.
    Clustering algorithms for massively parallel architectures including GPU nodes (2018). Last accessed March 2018
  21. 21.
    CUDA-MI (2018). Last accessed March 2018
  22. 22.
    Curk T, Rot G, Zupan B (2011) SNPsyn: detection and exploration of SNP–SNP interactions. Nucleic Acids Res 39(suppl_2):W444–W449PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Dagum L, Menon R (1998) OpenMP: an industry standard API for shared-memory programming. IEEE Comput Sci Eng 5(1):46–55CrossRefGoogle Scholar
  24. 24.
    Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRefGoogle Scholar
  25. 25.
    Dudley JT, Pouliot Y, Chen R, Morgan AA, Butte AJ (2010) Translational bioinformatics in the cloud: an affordable alternative. Genome Med 2(8):51PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Edge: R package for identifying differentially expressed genes from genome-wide gene expression profiling studies (2018). Last accessed March 2018
  27. 27.
    epiGPU v2.0 (2018). Last accessed March 2018
  28. 28.
    EPISNPmpi Homepage (2018). Last accessed March 2018
  29. 29.
    fast-mRMR (2018). Last accessed March 2018
  30. 30.
    FastEpistasis Homepage (2018). Last accessed March 2018
  31. 31.
    FastGCN for gene co-expression network (2018). Last accessed March 2018
  32. 32.
    Ferreira R, Vendramini JCG (2010) FPGA-accelerated attractor computation of scale free gene regulatory networks. In: 2010 international conference on field programmable logic and applications (FPL), pp 550–555Google Scholar
  33. 33.
    Galizia A, D’Agostino D, Clematis A (2015) An MPI–CUDA library for image processing on HPC architectures. J Comput Appl Math 273:414–427CrossRefGoogle Scholar
  34. 34.
    GBOOST Homepage (2018). Last accessed March 2018
  35. 35.
    Ghemawat S, Gobioff H, Leung S-T (2003) The Google file system. SIGOPS Oper Syst Rev 37(5):29–43CrossRefGoogle Scholar
  36. 36.
    González-Domínguez J, Martín MJ (2017) MPIGeneNet: parallel calculation of gene co-expression networks on multicore clusters. IEEE/ACM Trans Comput Biol Bioinform 15(5):1732–1737Google Scholar
  37. 37.
    González-Domínguez J, Schmidt B (2015) GPU-accelerated exhaustive search for third-order epistatic interactions in case–control studies. J Comput Sci 8:93–100CrossRefGoogle Scholar
  38. 38.
    González-Domínguez J, Schmidt B, Kässens JC, Wienbrandt L (2014) Hybrid CPU/GPU acceleration of detection of 2-SNP epistatic interactions in GWAS. In: 2014 European conference on parallel processing (Euro-Par), pp 680–691Google Scholar
  39. 39.
    Gonzalez-Dominguez J, Wienbrandt L, Kassens JC, Ellinghaus D, Schimmler M, Schmidt B (2015) Parallelizing epistasis detection in GWAS on FPGA and GPU-accelerated computing systems. IEEE/ACM Trans Comput Biol Bioinform 12(5):982–994PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    González-Domínguez J, Ramos S, Touriño J, Schmidt B (2016) Parallel pairwise epistasis detection on heterogeneous computing architectures. IEEE Trans Parallel Distrib Syst 27(8):2329–2340CrossRefGoogle Scholar
  41. 41.
    GPU3SNP: exhaustive search for third order epistatic interactions using CUDA (2018). Last accessed March 2018
  42. 42.
    Greene CS, Sinnott-Armstrong NA, Himmelstein DS, Park PJ, Moore JH, Harris BT (2010) Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS. Bioinformatics 26(5):694–695PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Guo X, Meng Y, Yu N, Pan Y (2014) Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering. BMC Bioinformatics 15(1):102PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Guzzi PH, Cannataro M (2010) Parallel Pre-processing of Affymetrix microarray data. In: 2010 European conference on parallel processing, Euro-Par, pp 225–232Google Scholar
  45. 45.
    Harvey BS, Ji S-Y (2017) Cloud-scale genomic signals processing for robust large-scale cancer genomic microarray data analysis. IEEE J Biomed Health Inform 21(1):238–245PubMedCrossRefGoogle Scholar
  46. 46.
    Hemani G, Theocharidis A, Wei W, Haley C (2011) EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards. Bioinformatics 27(11):1462–1465PubMedCrossRefGoogle Scholar
  47. 47.
    Hendrix W, Palsetia D, Patwary MdMA, Agrawal A, Liao W-K, Choudhary A (2013) A scalable algorithm for single-linkage hierarchical clustering on distributed-memory architectures. In: 2013 IEEE symposium on large-scale data analysis and visualization (LDAV), pp 7–13Google Scholar
  48. 48.
    Hu X, Liu Q, Zhang Z, Li Z, Wang S, He L, Shi Y (2010) SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder. Cell Res 20(7):854PubMedCrossRefGoogle Scholar
  49. 49.
    Hussain HM, Benkrid K, Seker H, Erdogan AT (2011) FPGA implementation of K-means algorithm for bioinformatics application: an accelerated approach to clustering microarray data. In: 2011 NASA/ESA conference on adaptive hardware and systems (AHS), pp 248–255Google Scholar
  50. 50.
    Hussain HM, Benkrid K, Seker H (2013) Reconfiguration-based implementation of SVM classifier on FPGA for classifying microarray data. In: 2013 Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 3058–3061Google Scholar
  51. 51.
    Ingram J, Zhu M (2011) GPU accelerated microarray data analysis using random matrix theory. In: 2011 IEEE international conference on high performance computing and communications (HPCC), pp 839–844Google Scholar
  52. 52.
    Irizarry RA, Gautier L, Cope LM et al (2003) An R package for analyses of Affymetrix oligonucleotide arrays. In: The analysis of gene expression data. Springer, New York, pp 102–119CrossRefGoogle Scholar
  53. 53.
    Irrthum A, Wehenkel L, Geurts P et al (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS One 5(9):e12776PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Islam AKMT, Jeong B-S, Bari ATMG, Lim C-G, Jeon S-H (2015) MapReduce based parallel gene selection method. Appl Intell 42(2):147–156CrossRefGoogle Scholar
  55. 55.
    Jünger D, Hundt C, Domínguez JG, Schmidt B (2017) Speed and accuracy improvement of higher-order epistasis detection on CUDA-enabled GPUs. Clust Comput 20(3):1899–1908CrossRefGoogle Scholar
  56. 56.
    Kässens JC, Wienbrandt L, González-Domínguez J, Schmidt B, Schimmler M (2015) High-speed exhaustive 3-locus interaction epistasis analysis on FPGAs. J Comput Sci 9:131–136CrossRefGoogle Scholar
  57. 57.
    Katsigiannis S, Zacharia E, Maroulis D (2015) Grow-cut based automatic cDNA microarray image segmentation. IEEE Trans Nanobioscience 14(1):138–145PubMedCrossRefGoogle Scholar
  58. 58.
    Katsigiannis S, Zacharia E, Maroulis D (2017) MIGS-GPU: microarray image gridding and segmentation on the GPU. IEEE J Biomed Health Inform 21(3):867–874PubMedCrossRefGoogle Scholar
  59. 59.
    Kohlhoff KJ, Sosnick MH, Hsu WT, Pande VS, Altman RB (2011) CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms. Bioinformatics 27(16):2321–2322CrossRefGoogle Scholar
  60. 60.
    Kornaros G (2010) A soft multi-core architecture for edge detection and data analysis of microarray images. J Syst Archit 56(1):48–62CrossRefGoogle Scholar
  61. 61.
    Kumar M, Rath SK (2015) Classification of microarray using mapreduce based proximal support vector machine classifier. Knowl-Based Syst 89:584–602CrossRefGoogle Scholar
  62. 62.
    Kumar M, Rath NK, Rath SK (2016) Analysis of microarray leukemia data using an efficient mapreduce-based K-nearest-neighbor classifier. J Biomed Inform 60:395–409PubMedCrossRefGoogle Scholar
  63. 63.
    Lachmann A, Giorgi FM, Lopez G, Califano A (2016) ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics 32(14):2233–2235PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Laide S, McAllister J (2017) Multicore distributed dictionary learning: a microarray gene expression biclustering case study. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1168–1172Google Scholar
  65. 65.
    Leek JT, Monsen E, Dabney AR, Storey JD (2005) EDGE: extraction and analysis of differential gene expression. Bioinformatics 22(4):507–508PubMedCrossRefGoogle Scholar
  66. 66.
    Liang M, Zhang F, Jin G, Zhu J (2015) FastGCN: a GPU accelerated tool for fast gene co-expression networks. PLoS One 10(1):e0116776PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Liu B, Yu CW, Wang DZ, Cheung RCC, Yan H (2014) Design exploration of geometric biclustering for microarray data analysis in data mining. IEEE Trans Parallel Distrib Syst 25(10):2540–2550CrossRefGoogle Scholar
  68. 68.
    Ma L, Runesha HB, Dvorkin D, Garbe JR, Da Y (2008) Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies. BMC Bioinformatics 9(1):315PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Magis AT, Earls JC, Ko Y-H, Eddy JA, Price ND (2011) Graphics processing unit implementations of relative expression analysis algorithms enable dramatic computational speedup. Bioinformatics 27(6):872–873PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera RD, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7(1):S7PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Mauch V, Kunze M, Hillenbrand M (2013) High performance cloud computing. Futur Gener Comput Syst 29(6):1408–1416CrossRefGoogle Scholar
  72. 72.
    Meeus W, Van Beeck K, Goedemé T, Meel J, Stroobandt D (2012) An overview of today’s high-level synthesis tools. Des Autom Embed Syst 16(3):31–51CrossRefGoogle Scholar
  73. 73.
    Mejía-Roa E, Tabas-Madrid D, Setoain J, García C, Tirado F, Pascual-Montano A (2015) NMF-mGPU: non-negative matrix factorization on multi-GPU systems. BMC Bioinformatics 16(1):43PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Message Passing Interface Forum (1994) MPI: a Message Passing Interface standardGoogle Scholar
  75. 75.
    Misra S, Pamnany K, Aluru S (2015) Parallel mutual information based construction of genome-scale networks on the Intel® Xeon Phi™Coprocessor. IEEE/ACM Trans Comput Biol Bioinform 12(5):1008–1020PubMedCrossRefGoogle Scholar
  76. 76.
    Mitchell L, Sloan TM, Mewissen M, Ghazal P, Forster T, Piotrowski M, Trew AS (2011) A parallel random forest classifier for R. In: 2011 International workshop on emerging computational methods for the life sciences (ECMLS), pp 1–6Google Scholar
  77. 77.
    Mitchell L, Sloan TM, Mewissen M, Ghazal P, Forster T, Piotrowski M, Trew A (2014) Parallel classification and feature selection in microarray data using SPRINT. Concurr Comput Pract Experience 26(4):854–865CrossRefGoogle Scholar
  78. 78.
    Moore JH, Asselbergs FW, Williams SM (2010) Bioinformatics challenges for genome-wide association studies. Bioinformatics 26(4):445–455PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    MPIGeneNet: parallel tool to construct gene co-expression networks (2018). Last accessed March 2018
  80. 80.
    Multifactor dimensionality reduction (2018). Last accessed March 2018
  81. 81.
    Nickolls J, Buck I, Garland M, Skadron K (2008) Scalable parallel programming with CUDA. In: 35th International conference on computer graphics and interactive techniques (SIGGRAPH’08), pp 16:1–16:14Google Scholar
  82. 82.
    Niu S, Yang G, Sarma N, Xuan P, Smith MC, Srimani P, Luo F (2014) Combining Hadoop and GPU to preprocess large Affymetrix microarray data. In: 2014 IEEE international conference on big data (Big Data), pp 692–700Google Scholar
  83. 83.
    NMF-mGPU: non-negative matrix factorization on multi-GPU systems (2018). Last accessed March 2018
  84. 84.
    Orzechowski P, Boryczko K (2015) Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets. Bio-Algorithms Med-Syst 11(4):243–248Google Scholar
  85. 85.
    ParallABEL: an R library for generalized parallelization of genome-wide association studies (2018). Last accessed March 2018
  86. 86.
    Parallel DBSCAN Code Download (2018). Last accessed March 2018
  87. 87.
    Parallel hierarchical clustering code download (2018). Last accessed March 2018
  88. 88.
    Parallel OPTICS code download (2018). Last accessed March 2018
  89. 89.
    Parallelized preprocessing methods for affymetrix oligonucleotide array (2018). Last accessed March 2018
  90. 90.
    Park S, Shin S-Y, Hwang K-B (2012) CFMDS: CUDA-based fast multidimensional scaling for genome-scale data. BMC Bioinformatics 13(17):S23PubMedPubMedCentralGoogle Scholar
  91. 91.
    Patwary MA, Palsetia D, Agrawal A, Liao W-K, Manne F, Choudhary A (2012) A new scalable parallel DBSCAN algorithm using the disjoint-set data structure. In: 2012 International conference on high performance computing, networking, storage and analysis (SC), p 62Google Scholar
  92. 92.
    Patwary MA, Palsetia D, Agrawal A, Liao W-K, Manne F, Choudhary A (2013) Scalable parallel OPTICS data clustering using graph algorithmic techniques. In: 2013 International conference for high performance computing, networking, storage and analysis (SC), pp 1–12Google Scholar
  93. 93.
    Pournara I, Bouganis C-S, Constantinides GA (2005) FPGA-accelerated Bayesian learning for reconstruction of gene regulatory networks. In: 2005 International conference on field programmable logic and applications (FPL), pp 323–328Google Scholar
  94. 94.
    Ramírez-Gallego S, Lastra I, Martínez-Rego D, Bolón-Canedo V, Benítez JM, Herrera F, Alonso-Betanzos A (2017) Fast-mRMR: fast minimum redundancy maximum relevance algorithm for high-dimensional big data. Int J Intell Syst 32(2):134–152CrossRefGoogle Scholar
  95. 95.
    Ray RB, Kumar M, Rath SK (2016) Fast computing of microarray data using resilient distributed dataset of Apache Spark. In: Recent advances in information and communication technology 2016. Springer, Cham, pp 171–182Google Scholar
  96. 96.
    Ray RB, Kumar M, Tirkey A, Rath SK (2016) Scalable information gain variant on spark cluster for rapid quantification of microarray. Procedia Comput Sci 93:292–298CrossRefGoogle Scholar
  97. 97.
    Rechkalov T, Zymbler M (2015) Accelerating medoids-based clustering with the Intel many integrated core architecture. In: 2015 International conference on application of information and communication technologies (AICT), pp 413–417Google Scholar
  98. 98.
    Ruchkys DP, Song SW (2003) A parallel solution to infer genetic network architectures in gene expression analysis. Int J High Perform Comput Appl 17(2):163–172CrossRefGoogle Scholar
  99. 99.
    Sangket U, Mahasirimongkol S, Chantratita W, Tandayya P, Aulchenko YS (2010) ParallABEL: an R Library for generalized parallelization of genome-wide association studies. BMC Bioinformatics 11(1):217PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    SHEsis main (2018). Last accessed March 2018
  101. 101.
    Schmidberger M, Vicedo E, Mansmann U (2009) affypara—a bioconductor package for parallelized preprocessing algorithms of Affymetrix microarray data. Bioinf Biol Insights 3:83CrossRefGoogle Scholar
  102. 102.
    Schüpbach T, Xenarios I, Bergmann S, Kapur K (2010) FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics 26(11):1468–1469PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Shi H, Schmidt B, Liu W, Müller-Wittig W (2011) Parallel mutual information estimation for inferring gene regulatory networks on GPUs. BMC Res Notes 4(1):189PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Shvachko K, Kuang H, Radia S, Chansler R (2010) The Hadoop distributed file system. In: IEEE 26th symposium on mass storage systems and technologies (MSST’2010), pp 1–10Google Scholar
  105. 105.
    Sluga D, Curk T, Zupan B, Lotric U (2014) Heterogeneous computing architecture for fast detection of SNP-SNP interactions. BMC Bioinformatics 15(1):216PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Stone JE, Gohara D, Shi G (2010) OpenCL: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12(3):66–73PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Tamada Y, Imoto S, Araki H, Nagasaki M, Print C, Charnock-Jones DS, Miyano S (2011) Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers. IEEE/ACM Trans Comput Biol Bioinform 8(3):683–697PubMedCrossRefGoogle Scholar
  108. 108.
    The Apache Software Foundation (2006). Apache HadoopGoogle Scholar
  109. 109.
    The real-time systems and Image Analysis Lab (2018). Last accessed March 2018
  110. 110.
    Top-scoring pair and top-scoring triple on the graphics processing unit (2018). Last accessed March 2018
  111. 111.
    Upton A, Trelles O, Cornejo-García JA, Perkins JR (2015) High-performance computing to detect epistasis in genome scale data sets. Brief Bioinform 17(3):368–379PubMedCrossRefGoogle Scholar
  112. 112.
    Wang S, Pandis I, Johnson D, Emam I, Guitton F, Oehmichen A, Guo Y (2014) Optimising parallel R correlation matrix calculations on gene expression data using mapreduce. BMC Bioinformatics 15(1):351PubMedPubMedCentralCrossRefGoogle Scholar
  113. 113.
    Wu H-C, Wei X-G, Chan S-C (2017) Novel consensus gene selection criteria for distributed GPU partial least squares-based gene microarray analysis in diffused large B cell lymphoma (DLBCL) and related findings. IEEE/ACM Trans Comput Biol Bioinform 15(6):2039–2052PubMedCrossRefGoogle Scholar
  114. 114.
    Yung LS, Yang C, Wan X, Yu W (2011) GBOOST: a GPU-based tool for detecting gene–gene interactions in genome–wide case control studies. Bioinformatics 27(9):1309–1310PubMedPubMedCentralCrossRefGoogle Scholar
  115. 115.
    Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M et al (2012) Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: 9th USENIX symposium on networked systems design and implementation (NSDI’12), pp 15–28Google Scholar
  116. 116.
    Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A et al (2016) Apache Spark: a unified engine for Big Data processing. Commun ACM 59(11):56–65CrossRefGoogle Scholar
  117. 117.
    Zhang C, Li P, Rajendran A, Deng Y, Chen D (2006) Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data. BMC Bioinformatics 7(4):S15PubMedPubMedCentralCrossRefGoogle Scholar
  118. 118.
    Zheng M, Zhuo M, Zhang S, Liu G (2017) Inferring genome-wide gene regulatory networks with GPU or CPU parallel algorithm. In: 2017 International conference on computer network, electronic and automation (ICCNEA), pp 54–58Google Scholar
  119. 119.
    Zhou Z, Liu G, Su L, Yan L, Han L (2013) CChi: an efficient cloud epistasis test model in human genome wide association studies. In: 2013 International conference on biomedical engineering and informatics (BMEI), pp 787–791Google Scholar
  120. 120.
    Zhou Z, Liu G, Su L (2016) A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by mapreduce framework. Int J Comput Math 93(3):511–523CrossRefGoogle Scholar
  121. 121.
    Zola J, Aluru M, Sarje A, Aluru S (2010) Parallel information-theory-based construction of genome-wide gene regulatory networks. IEEE Trans Parallel Distrib Syst 21(12):1721–1733CrossRefGoogle Scholar
  122. 122.
    Zoppoli P, Morganella S, Ceccarelli M (2010) TimeDelay-ARACNE: reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics 11(1):154PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jorge González-Domínguez
    • 1
    Email author
  • Roberto R. Expósito
    • 1
  1. 1.Grupo de Arquitectura de ComputadoresCITIC, Universidade da CoruñaA CoruñaSpain

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