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WIREs Data Mining Knowl Discov

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Data mining and life sciences applications on the grid

Advanced Review

Alessia Sarica, Pietro Hiram Guzzi, Mario Cannataro

Published Online: Apr 18 2013

DOI: 10.1002/widm.1090

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Abstract Data mining (DM) is increasingly used in the analysis of data generated in life sciences, including biological data produced in several disciplines such as genomics and proteomics, medical data produced in clinical practice, and administrative data produced in health care. The difficulty in mining such data is twofold. First of all, data in life sciences are inherently heterogeneous, spanning from molecular level data to clinical and administrative data. Second, data in life sciences are produced at an increasing rate and data repositories are becoming very large. Thus, the management and analysis of such data is becoming a main bottleneck in biomedical research. The main goal of this paper is to review the main methodologies to mine life sciences data and the ways they are coupled to high‐performance infrastructures and systems that result in an efficient analysis. This paper recalls basic concepts of DM, grids, and distributed DM on grids, and reviews main approaches to mine biomedical data on high‐performance infrastructures with special focus on the analysis of genomics, proteomics, and interactomics data, and the exploration of magnetic resonance images in neurosciences. The paper can be of interest both to bioinformaticians, who can learn how to exploit high performance infrastructures to mine life sciences data, and to computer scientists, who can address the heterogeneity and the high volumes of life sciences data at the data management, algorithm, and user interface layers. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Data Mining Software Tools Application Areas > Health Care Technologies > Computer Architectures for Data Mining

The Weka explorer interface.

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ROC graph related to the classification of breast cancer dataset generated by Weka.

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Confusion matrix generated by Weka by applying SVM (SMO Weka Classifier) on the breast cancer dataset available on the Weka website.

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Workflow diagram for the knowledge discovery in neuroscience.

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The RapidMiner interface.

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References

Witten, IH,Frank, E.Data Mining. Practical Machine Learning Tools and Techniques.2nd ed.San Francisco, CA: Morgan Kaufmann;2005.
Han, J,Kamber, M. In: Jim Gray,, series editor.Data Mining: Concepts and Techniques.2nd ed.San Francisco, CA: Morgan Kaufmann Publishers;2006. ISBN 1‐55860‐901‐6.
Cannataro, M, ed.Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, Medical Information Science Reference.Hershey, PA: IGI Global Press;2009.
Foster, I,Kesselman, C.The Grid 2: Blueprint for a New Computing Infrastructure.San Francisco, CA: Morgan Kaufmann Publishers;2003.
Thain, D,Tannenbaum, T,Livny, M.Distributed computing in practice: the Condor experience.Concurr Comput Pract Exp2005,17:323–356.
Couvares, P,Kosar, T,Roy, A,Jeff Weber, J,Wenger, K.Workflow Management in Condor. In:Taylor, I,Deelman, E,Gannon, D,Shields, M, eds.Workflows for e‐Science.Berlin, Deutschland: Springer Press;2007. ISBN: 1‐84628‐519‐4.
De Roure, D,Jennings, NR,Shadbolt, NR.The semantic grid: past, present, and future.Proc IEEE2005,93:669–681.
Berners‐Lee, T,Hendler, J,Lassila, O.The Semantic Web.New York: Scientific American;2001.
De Roure, D,Jennings, NR,Shadbolt, N.The semantic grid: a future e‐Science infrastructure. In:Berman, F,Hey, AJG,Fox, G, eds.Grid Computing: Making The Global Infrastructure a Reality.John Wiley %26 Sons;2003,437–470.
Ahmed, M,Chowdhury, ASMR,Ahmed, M,Rafee, MMH.An advanced survey on cloud computing and state‐of‐the‐art research issues.Int J Comp Sci2012,9:201–207.
Foster, I,Zhao, Y,Raicu, I,Lu, S.Cloud computing and grid computing 360‐degree compared.Grid Comput Environ Workshop;2008,1–10.
Mell, P,Grance, T.The NIST definition of cloud computing. Recommendations of the National Institute of Standards and Technology;2011. Available at:http://csrc.nist.gov/publications/nistpubs/800‐145/SP800‐145.pdf. (Accessed March 13, 2013).
Schwiegelshohn, U,Badia, RM,Bubak, M,Danelutto, M,Dustdar, S,Gagliardi, F,Geiger, A,Ladislav Hluchy, L,Kranzlmuller, D,Laure, E, et al.Perspectives on grid computing.Future Gen Comp Syst2010,26:1104–1115.
Paul, S.Parallel and Distributed Data Mining.in Kimito Funatsu (ed.), New Fundamental Technologies in data Mining, ISBN: 978‐953‐307‐547‐1, InTech. Coimbatore, India; 2011. Available at:http://cdn.intechopen.com/pdfs/13261/InTech‐Para‐llel_and_distributed_data_mining.pdf. (Accessed March 13, 2013).
Stankovski, V,Swain, M,Kravtsov, V,Niessen, T,Wegener, D,Kindermann, J,Dubitzky, W.Grid‐enabling data mining applications with DataMiningGrid: an architectural perspective.Sci Direct Future Gen Comp Syst2008,24:259–279.
Cannataro, M,Talia, D.Knowledge Grid: an architecture for distributed knowledge discovery.Commun ACM2003,46:89–93.
Stankovski, V,Swain, M,Kravtsov, V,Niessen, T,Wegener, D,Rohm, M,Trnkoczy, J,May, M,Franke, J,Schuster, A,Dubitzky, W.EEE Int Comput2008,12:69–76 .
Moore, R.Knowledge‐based grids. Technical Report San Diego Supercomputer Center, SDCS TR‐2001‐2;2001. Available at:http://legacy.sdsc.edu/techreports/TR‐2001‐02.doc.pdf. (Accessed March 13, 2013).
Berman, F.From TeraGrid to Knowledge Grid.CACM2001,44:27–28.
Johnston, WE.Computational and data grids in large‐scale science and engineering.Future Gen Comp Syst2002,18:1085–1100.
Cannataro, M,Congiusta, A,Pugliese, A,Talia, D,Trunfio, P.Distributed data mining on grids: services, tools, and applications.IEEE Trans Syst Man Cybern B2004,34:2451–2465.
Ellisman, M,Brady, M,Hart, D,Lin, FP,Müller, M,Smarr, L.The emerging role of biogrids.Commun ACM2004,47:52–57.
Cannataro, M,Guzzi, PH,Lobosco, M,Weber dos Santos, R.GridSnake: a Grid‐based Implementation of the Snake Segmentation Algorithm.22nd IEEE International Symposium on Computer‐Based Medical Systems, 2009. CBMS 2009.Albuquerque, NM;2009,1–6.
Teixeira, GM,Pommeranzembaum, IR,de Oliveira, BL,Lobosco, M,dos Santos, RW.Automatic segmentation of cardiac mri using snakes and genetic algorithms. In:Bubak, M,van Albada, GD,Dongarra, J,Sloot, PMA, eds.ICCS (3), volume 5103 of Lecture Notes in Computer Science.Berlin, Deutschland: Springer;2008,168–177.
Dudley, JT,Butte, AJ.In silico research in the era of cloud computing.Nat Biotechnol2010,28:1181–1185.
Schatz, MC,Langmead, B,Salzberg, SL.Cloud computing and the DNA data race.Nat Biotechnol 2010,28:691–693.
Krampis, K,Booth, T,Chapman, B,Tiwari, B,Bicak, M,Field, D,Nelson, KE.Cloud BioLinux: pre‐configured and on‐demand bioinformatics computing for the genomics community.BMC Bioinform 2012,13:42.
Talia, D,Trunfio, P.Service‐Oriented Distributed Knowledge Discovery.London, UK: Chapman %26 Hall/CRC Data Mining and Knowledge Discovery Series;2012.
Hall, M,Frank, E,Holmes, G,Pfahringer, B,Reutemann, P,Witten, IH.The WEKA data mining software: an update.SIGKDD Explor2009,11: 1–18.
Kretschmann, E,Fleischmann, W,Apweiler, R.Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS‐PROT.Bioinformatics2001,17:920–6.
Bazzan, AL,Engel, PM,Schroeder, LF,da Silva, SC.Automated annotation of keywords for proteins related to mycoplasmataceae using machine learning techniques.Bioinformatics2002,18:S35–S43.
Tobler, JB,Molla, MN,Nuwaysir, EF,Green, RD,Shavlik, JW.Evaluating machine learning approaches for aiding probe selection for gene‐expression arrays.Bioinformatics2002,18:S164–S71.
Bekaert, M,Bidou, L,Denise, A,Duchateau‐Nguyen, G,Forest, JP,Froidevaux, C, et al.Towards a computational model for ‐1 eukaryotic frameshifting sites.Bioinformatics2003,19:327–35.
Taylor, J,King, RD,Altmann, T,Fiehn, O.Application of metabolomics to plant genotype discrimination using statistics and machine learning.Bioinformatics2002,18:S241–S248.
Frank, E,Hall, M,Trigg, L,Holmes, G,Witten, IH.Data mining in bioinformatics using Weka.Bioinform Appl Note2004,20:2479–2481.
Pyka, M,Balz, A,Jansen, A,Krug, A,Hüllermeier, E.A WEKA interface for fMRI data.Neuroinformatics2012,10:409–413.
Celis, S,Musicant, DR.Weka‐parallel: machine learning in parallel. Available at: http://weka‐parallel.sourceforge.net/report.pdf. (Accessed March 13, 2013).
Khoussainov, R,Zuo, X,Kushmerick, N.Grid‐enabled Weka: a toolkit for machine learning on the Grid.ERCIM News2004,59:47–48.
Pérez, MS,Sánchez, A,Robles, V,Herrero, P,Peña, JM.Design and Implementation of a Data Mining Grid‐aware Architecture.FutureGen Comp Syst2007,23:42–47.
Pérez, MS,Sánchez, A,Robles, V,Herrero, P,Peña, JM.Adapting the Weka data mining toolkit to a grid based environment.Adv Web Intell Lect Notes Comput Sci2005,3528:492–497.
Talia, D,Trunfio, P,Verta, O.The Weka4WS framework for distributed data mining in service‐oriented Grids.Concurr Comput Pract Exp2008,20:1933–1951.
Mierswa, I,Scholz, M,Klinkenberg, R,Wurst, M,Euler, T.YALE: rapid prototyping for complex data mining tasks.Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD‐06);2006.
Di Martino, MT,Arbitrio, M,Leone, E,Guzzi, PH,Saveria Rotundo, M,Ciliberto, D,Tomaino, V,Fabiani, F,Talarico, D,Sperlongano, P, et al.Single nucleotide polymorphisms of ABCC5 and ABCG1 transporter genes correlate to irinotecan‐associated gastrointestinal toxicity in colorectal cancer patients: A DMET microarray profiling study.Cancer Biol Ther2011,12:780–778.
Guzzi, PH,Mario Cannataro, M.Parallel pre‐processing of affymetrix microarray data.Proc Eur Workshops Lect Notes Comp Sci;2010,6586.
Kreil, DP,Russell, RR.Tutorial section: there is no silver bullet a guide to low‐level data transforms and normalization methods for microarray data.Brief Bioinform2005,6:86–97
Sangurdekar, D,Srienc, F,Khodursky, A.A classification based framework for quantitative description of large‐scale microarray data.Genome Biol2006,7:R32+.
Schmidberger, M,Vicedo, E,Mansmann, U.affypara‐a bioconductor package for parallelized preprocessing algorithms of affymetrix microarray data.Bioinform Biol Insights2009,30:83–87.
Guzzi, PH,Mario Cannataro, M.mu‐cs: An extension of the tm4 platform to manage affymetrix binary data.BMC Bioinform2010,11:315.
Aebersold, R,Mann, M.Mass spectrometry‐based proteomics.Nature2003,422:198–207.
Cannataro, M.Computational proteomics: management and analysis of proteomics data.Brief Bioinform2008,9:97–101.
Veltri, P,Cannataro, M,Tradigo, G.Sharing mass spectrometry data in a grid‐based distributed proteomics laboratory.Inf Process Manage2007,43:577–591.
Pedrioli, PG,Eng, JK,Hubley, R,Vogelzang, M,Deutsch, EW,Raught, B,Pratt, B,Nilsson, E,Angeletti, RH,Apweiler, R.A common open representation of mass spectrometry data and its application to proteomics research.Nat Biotechnol2004,22:1459–1466.
Orchard, S,Montecchi‐Palazzi, L,Hermjakob, H,Apweiler, R.The use of common ontologies and controlled vocabularies to enable data exchange and deposition for complex proteomic experiments.Pac Symp Biocomput2005,186–196.
Martens, L,Chambers, M,Sturm, M,Kessner, D,Levander, F,Shofstahl, J,Tang, WH,Römpp, A,Neumann, S,Pizarro, AD, et al.mzML—a community standard for mass spectrometry data.Mol Cell Proteomics2011,10:R110.000133.
Barla, A,Jurman, G,Riccadonna, S,Chierici, M,Merler, S,Furlanello, C.Machine learning methods for predictive proteomics.Brief Bioinform2008,9:119–128.
Stevens, RD,Robinson, AJ,Goble, CA.myGrid: personalised bioinformatics on the information Grid.Bioinformatics2004,19:302–302C.
Aloisio, G,Cafaro, M,Epicoco, I,Fiore, S,Mirto, M.A services oriented system for bioinformatics applications on the grid. CACT‐NNL/CNR‐INFM, CMCC, University of Salento, Lecce, Italy.Stud Health Technol Inform2005,126:174–183.
Cannataro, M,Guzzi, PH,Mazza, T,Tradigo, G,Veltri, P.Using ontologies for preprocessing and mining spectra data on the grid.Future Gen Comp Syst2007,23:55–60.
Cannataro, M,Guzzi, PH.Data Management of Protein Interaction Networks.Hoboken, NJ: Wiley‐IEEE Computer Society Press, Wiley Book Series on Bioinformatics;2011.
Aittokallio, T,Schwikowski, B.Graph‐based methods for analysing networks in cell biology.Brief Bioinform2006,7:243–255.
Cannataro, M,Guzzi, PH,Veltri, P.Protein‐to‐protein interactions: Technologies, databases, and algorithms.ACM Comput Surv2010,43:1:1–1:36.
Cerami, EG,Bader, GD,Gross, BE,Sander, C.cPath: open source software for collecting, storing, and querying biological pathways.BMC Bioinform2006,7:497.
Guzzi, PH,Mina, M,Guerra, C,Cannataro, M.Semantic similarity analysis of protein data: assessment with biological features and issues.Brief Bioinform2012,13:569–585.
Nassa, G,Tarallo, R,Ambrosino, C,Bamundo, A,Ferraro, L,Paris, O,Ravo, M,Guzzi, PH,Cannataro, M,Baumann, M, et al.A large set of estrogen receptor β‐interacting proteins identified by tandem affinity purification in hormone‐responsive human breast cancer cell nuclei.Proteomics2011,11:159–65.
Orchard, S,Kerrien, S,Jones, P,Ceol, A,Chatr‐Aryamontri, A,Salwinski, L,Nerothin, J,Hermjakob, H.Submit your interaction data the IMEx way: a step by step guide to trouble‐free deposition.Proteomics2007,7:28–34.
Hermjakob, H,Montecchi‐Palazzi, L,Bader, G,Wojcik, J,Salwinski, L,Ceol, A,Moore, S,Orchard, S,Sarkans, U,von Mering, C, et al.The HUPO PSI`s molecular interaction format–a community standard for the representation of protein interaction data.Nat Biotechnol2004,22:177–183.
Ciriello, G,Mina, M,Guzzi, PH,Cannataro, M,Guerra, C.AlignNemo: a local network alignment method to integrate homology and topology.PLoS ONE2012,7:e38107.
Cannataro, M,Guzzi, PH,Veltri, P.IMPRECO: distributed prediction of protein complexes.Future Gen Comp Syst2012,26:434–440.
Kalaev, M,Smoot, M,Ideker, T,Sharan, R.NetworkBLAST: comparative analysis of protein networks.Bioinformatics2008,24:594–596.
Flannick, J,Novak, A,Srinivasan, BS,McAdams, HH,Batzoglou, S.Graemlin: general and robust alignment of multiple large interaction networks.Genome Res2006,16:1169–1181.
Cannataro, M,Guzzi, PH,Mazza, T,Tradigo, G,Veltri, P.Preprocessing of mass spectrometry proteomics data on the grid.18th IEEE International Symposium on Computer‐Based Medical Systems (CBMS`05).Dublin, Ireland:Trinity College Dublin;2005,549–554.
Cannataro, M,Guzzi, PH.Parallel preprocessing of affymetrix microarray data.Workshop HIBB 2010 (High performance bioinformatics and biomedicine), held in conjunction with Int Conference Euro‐Par 2010.Ischia:Euro‐Par 2010 Workshops, Lecture Notes in Computer Science;2010,6586:225–232.
Ye, J,Wu, T,Li, J,Chen, K.Machine learning approaches for the neuroimaging study of Alzheimer`s disease.IEEE Comp Soc2011,44:99–101.
Akil, H,Martone, ME,Van Essen, DC.Challenge and opportunities in mining neuroscience data.Science2011,331:708–712.
Symms, M,Jäger, HR,Schmierer, K,Yousry, TA.A review of structural magnetic resonance neuroimaging.J Neurol Neurosurg Psychiat2004,75:1235–1244.
Roy, CS,Sherrington, CS.On the regulation of blood supply of the brain.J Physiol1890,1:85–108.
Stejskal, EO,Tanner, JE.Spin diffusion measurements: spin echoes in the presence of a time‐dependent field gradient.J Chem Phys1965,42:288–292.
Megalooikonomou, V,Ford, J,Shen, L,Makedon, F.Data mining in brain imaging.Stat Method Med Res2000,9:359–394.
Hsu, W,Li Lee, M,Zhang, J.Image Mining: Trends and Developments.J Intell Inf Syst2002,19:7–23.
Kakimoto, M,Morita, C,Tsukimoto, H.Data mining from functional brain images.The Sixth ACM SIGKDD Int Conf on Knowled Discov and Data Mining, Workshop on Multimedia Data Mining;2000.
Morra, JH,Tu, Z,Apostolova, LG,Green, AE,Toga, AW,Thompson, PM.Comparison of AdaBoost and support vector machines for detecting Alzheimer`s disease through automated hippocampal segmentation.IEEE Trans Med Imaging2010,29:30–43.
Cuingnet, R,Chupin, M,Benali, H,Colliot, O.Spatial and anatomical regularization of SVM from brain image analysis.Proc NIPS;2010,460–468.
Swain, R,Jena, L,Kamila, NK.Cognitive states from brain images: SVM approach.Int J Comput Commun Technol2010,2(Special Issue):194–199.
Pyka, M,Balz, A,Jansen, A,Krug Hüllermeier, E.A WEKA interface for fMRI data.Neuroinformatics2012,10:409–413.
Maroco, J,Silva, D,Rodrigues, A,Guerreiro, M,Santana, I,de Mendoça, A.Data mining methods in the prediction of Dementia: a real‐data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests.BMC Res Notes2011,4:299.
Pereira, F,Mitchell, T,Botvinick, M.Machine learning classifiers and fMRI: a tutorial overview.Neuroimage2009,45:S199–S209.
Cristianini, N,Shawe‐Taylor, J.An Introduction to Support Vector Machines and Other Kernel‐Based Learning Methods.Cambridge, UK: Cambridge University Press; 2000.
Vapnik, V.Estimation of Dependences Based on Empirical Data [in Russian].Nauka, Moscow:Springer Verlag (English translation—New York: Springer Verlag; 1982);1979.
Vapnik, V.The Nature of Statistical Learning Theory.New York:Springer‐Verlag;1995.
Vapnik, V.Statistical Learning Theory.New York:John Wiley and Sons, Inc.;1998.
Meligy, A,Al‐Khatib, M.A grid‐based distributed SVM data mining algorithm.Eur J Sci Res.2009,313–321.
Lu, Y,Roychowdhury, V,Vandenberghe, L.Distributed parallel support vector machines in strongly connected networks.IEEE Trans Neural Netw2008,19:1167–1178.
Chang, EY,Zhu, K,Wang, H,Bai, H,Li, J,Zhihuan Qiu, Z,Cui, H.PSVM: Parallelizing Support Vector Machines on Distributed Computers.Beijing, China:Google Research.
Freund, Y,Schapire, RE.A short introduction to boosting.J Japan Soc Artif Intell1999,14:771–780.
Freund, Y,Schapire, RE.Experiments with a new boosting algorithm.Machine Learn: Proc Thirteen Int Conf;1996.
Falaki, H.AdaBoost Algorithm.Los Angeles, CA:Computer Science Department, University of California.
Fawcett, T.An introduction to ROC analysis.J Pattern Recogn Lett2006,27:861–874.
Cannataro, M,Comito, C,Guzzo, A,Veltri, P.Integrating ontology and workflow in PROTEUS, a grid‐based problem solving environment for bioinformatics.Proc Inf Technol: Coding and Comput Conf (ITCC 2004)2004,2:90–94.
Mastroianni, C,Talia, D,Trunfio, P.Managing heterogeneous resources in data mining applications on grids using xml‐based metadata.Nice, France:Proceedings of the 17th International Parallel and Distributed Processing Symposium (IPDPS);2003.
Cannataro, M,Veltri, P.MS‐Analyzer: composing and executing preprocessing and data mining services for proteomics applications on the grid.Concurr Comp Pract Exp2007,19:2047–2066.
Cannataro, M,Guzzi, PH,Mazza, T,Tradigo, G,Veltri, P.Managing ontologies for grid computing.Multiagent Grid Syst2006,2:29–44.

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