Drug discovery applications for KNIME: An open source data mining platform

Current Topics in Medicinal Chemistry

Volumn 12, Issue 18, 2012, Pages 1965-1978

  Mazanetz, Michael P ^a   Marmon, Robert J ^a   Reisser, Catherine B T ^a   Morao, Inaki ^a  

^a EVOTEC UK LTD   (United Kingdom)

Author keywords

Bioinformatics; Cheminformatics; Computational chemistry; Data mining; Drug discovery; KNIME

Indexed keywords

LEAD;

ARTICLE; BIOINFORMATICS; COMPUTER LANGUAGE; COMPUTER PROGRAM; DATA EXTRACTION; DATA MINING; FLUORESCENCE MICROSCOPY; KNOWLEDGE; MOLECULAR DOCKING; PUBLICATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SEQUENCE ANALYSIS; VALIDATION STUDY;

CHEMISTRY, PHARMACEUTICAL; DATA MINING; DRUG DISCOVERY; HIGH-THROUGHPUT SCREENING ASSAYS; SOFTWARE; WORKFLOW;

EID: 84874910700     PISSN: 15680266     EISSN: 18734294     Source Type: Journal    
DOI: 10.2174/156802612804910331     Document Type: Article

References (139)

1. The PubChem Project. USA: National Center for Biotechnology Information. PubChem Substance database. http://www.ncbi.nlm. nih.gov/sites/entrez?term=all%5Bfilt%5D&cmd=search&db=pcsub stance (Accessed July 12, 2012).
2. The PubChem Project. USA: National Center for Biotechnology Information. PubChem Compound database. http://www.ncbi.nlm. nih.gov/sites/entrez?term=all%5Bfilt%5D&cmd=search&db=pcco mpound (Accessed July 12, 2012).
3. The PubChem Project. USA: National Center for Biotechnology Information. PubChem BioAssay database. http://www.ncbi.nlm.nih.gov/sites/entrez?term=all%5Bfilt%5D&cmd=search&db=pcass ay (Accessed July 12, 2012).
4. RCSB Protein Data Bank -RCSB PDB. http://www.rcsb.org/pdb/ home/home.do (Accessed July 30, 2012).
5. Schadt, E.E.; Linderman, M.D.; Sorenson, J.; Lee, L.; Nolan, G.P. Computational solutions to large-scale data management and analysis. Nat. Rev. Gent., 2010, 11, 647-657.
6. Tiwari, A.; Sekhar, A.K. Workflow based framework for life science informatics. Comput. Biol. Chem., 2007, 31(5-6),305-319.
7. Chen, X.; Ye, Y.; Williams, G; Xu, X. A Survey of open source data mining systems. Lect. Notes Comput. Sc., 2009, 4819, 3-14.
8. Mikut, R.; Reischl, M. Data mining tools. WIREs Data Mining Knowl. Discov., 2011, 1, 431-443.
9. Zupan, B.; Demsar, J. Open-source tools for data mining. Clin. Lab. Med., 2008, 28, 37-54.
10. Jeliazkova, N.; Jaworska, J.; Worth, A.P. In: In Silico Toxicology: Principles and Applications; Cronin, M.T.D.; Madden J.C., Ed.; RSC Publishing: Cambridge, UK, 2010; Chpt. 17, 408-445.
11. Frey, J.G.; Bird, C.L. Web-based services for drug design and discovery. Expert Opin. Drug. Discov.,2011, 6(9), 885-895.
12. Claus, B.L.; Underwood, D.J. Discovery informatics: its evolving role in drug discovery. Drug Discov. Today, 2002, 7(18), 957-966.
13. Berthold, M.R.; Cebron, N.; Dill, F.; Gabriel, T.R.; Kötter, T.; Meinl, T.; Ohl, P.; Thiel, K.; Wiswedel, B. KNIME - The Konstanz Information Miner. SIGKDD Explorations, 2009, 11(1), 26-31
14. st ed.; KNIME Press: Zurich, 2010.
15. Murray-Rust, P.; Townsend, J.; Adams, A.; Phadungsukanan, W.; Thomas, J. The semantics of Chemical Markup Language (CML): dictionaries and conventions, J. Cheminform., 2011, 3(43).
16. InChI: http://www.iupac.org/home/publications/e-resources/ inchi. html (Accessed July 12, 2012).
17. OpenBabel: http://www.jcheminf.com/content/3/1/33 (Accessed July 12, 2012).
18. Bioformats: http://loci.wisc.edu/bio-formats/formats (Accessed July 12, 2012).
19. Data Mining Group. Predictive Model Markup Language (PMML): http://www.dmg.org/ (Accessed July 12, 2012).
20. Sieb, C.; Meinl, T.; Berthold, M.R.; Parallel and Distributed data pipelining with KNIME, Mediterranean Journal of Computers and Networks, 2007, 3(2), 43-51.
21. st ed.; Morgan Kaufman Publishers, San Francisco, 1999.
22. nd Ed. Morgan Kaufman Publishers, San Francisco, USA, 2005.
23. R Development Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2011: http://cran.r-project.org/ (Accessed July 12, 2012).
24. D. J FreeChart Developer Guide. Object Refinery Limited, Berkeley, CA., 2005. http://www.jfree.org/jfreechart/ (Accessed July 12, 2012).
25. st ed.; KNIME Press: Zurich Switzerland, 2012.
26. BIRT Project: http://www.eclipse.org/birt/phoenix/intro/ (Accessed July 12, 2012).
27. KNIME Forum and Documentation: http://tech.knime.org/ (Accessed July 12, 2012).
28. st ed.; KNIME Press, Zurich, 2011.
29. Lindenbaum, P.; Le Scouarnec, S.; Portero, V.; Redon, R. Knime4Bio: a set of custom nodes for the interpretation of next-generation sequencing data with KNIME. Bioinformatics, 2011, 27(22), 3200-3201.
30. Curcin, V.; Ghanem, M.; Guo, Y. Web services in the life sciences. Drug Discov. Today., 2005, 10(12), 865-871.
31. Guha, R. Generating, using and visualizing molecular information in R. R News, 2006, 6(3), 28-33.
32. Bioconductor. Open Source software for Bioinformatics: http://www.bioconductor.org/ (Accessed July 12, 2012).
33. Steinbeck, C.; Han, Y.; Kuhn, S.; Horlacher, O.; Luttmann, E.; Willighagen, E.L. The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics. J. Chem. Inf. Comput. Sci., 2003, 43(2), 493-500.
34. Steinbeck, C.; Hoppe, C.; Kuhn, S.; Floris, M.; Guha, R.; Willigh-agen, E.L. Recent Developments of the Chemistry Development Kit (CDK) -An Open-Source Java Library for Chemo-and Bioin-formatics. Curr. Pharm. Des., 2006, 12(17), 2111-2120.
35. Network Mining KNIME forum: http://tech.knime.org/network-mining (Accessed July 12, 2012).
36. KNIME Text Processing, KNIME forum: http://tech.knime.org/ knime-text-processing-0 (Accessed July 12, 2012).
37. Berthold, M.R.; Wiswedel, B.; Patterson, D.E. Interactive Exploration of Fuzzy Clusters Using Neighborgrams, Fuzzy Set. Syst., 2005, 149(1), 21-37.
38. Novamechanics: http://www.novamechanics.com/Home.php (Accessed July 12, 2012).
39. Erlwood Cheminformatics, KNIME forum: http://tech.knime.org/ forum/erlwood (Accessed July 12, 2012).
40. GGA Software Services LLC: http://ggasoftware.com/opensource/indigo (Accessed July 12, 2012).
41. RDKit. Open-Source Cheminformatics and Machine Learning: http://www.rdkit.org/ (Accessed July 12, 2012).
42. Jagla, B; Wiswedel, B; Coppée, J.Y. Extending KNIME for next-generation sequencing data analysis. Bioinformatics, 2011, 27(20), 2907-2909.
43. KNIME Extensions for Next Generation Sequencing, KNIME forum: http://tech.knime.org/community/next-generationsequencing (Accessed July 12, 2012).
44. Stoeter, M.; Niederlein, A.; Barsacchi, R.; Meyenhofer, F.; Brandl, H.; Bickle, M. In: Target Identification and Validation in Drug Discovery, Methods in Molecular Biology Series; Colombo, R.; Walker, J.; Moll, J., Eds.; Humana Press, USA, 2012, in press
45. Image Processing and Analysis in KNIME, KNIME forum: http://tech.knime.org/community/image-processing (Accessed July 12, 2012).
46. MathWorks, Inc.: http://www.mathworks.co.uk (Accessed July 12, 2012).
47. GNU Octave: http://www.gnu.org/software/octave/ (Accessed July 12, 2012).
48. Vernalis (R&D) Ltd and Enspiral Discovery Ltd: http://enspiral-discovery.com/vernalis.htm (Accessed July 12, 2012).
49. th ACS National Meeting, Washington, DC, August 16-20, 2009.
50. Togo, Computation Biology Researach Center, AIST: http:// tgweb01.cbrc.jp/active.eng.html (Accessed July 12, 2012).
51. Sequime. http://sourceforge.net/apps/mediawiki/sequime/index.php?title=Ma in_Page (Accessed July 12, 2012).
52. HiTS: http://code.google.com/p/hits/ (Accessed July 12, 2012).
53. Visone: http://visone.info/wiki/index.php/Knime_menu (Accessed July 12, 2012).
54. BioSolveIT GmbH: http://www.biosolveit.de/KNIME/ (Accessed July 12, 2012).
55. ChemAxon Kft: http://www.chemaxon.com (Accessed July 12, 2012).
56. Chemical Computing Group Inc.: http://www.chemcomp.com (Accessed July 12, 2012).
57. Cresset Group: http://www.cresset-group.com (Accessed July 12, 2012).
58. Dotmatics: http://www.dotmatics.com (Accessed July 12, 2012).
59. Infocom Corporation: http://infocom-science.jp/product/detail/jche mextensions_english.html (Accessed July 12, 2012).
60. Korilog SARL: http://www.korilog.com (Accessed July 12, 2012).
61. Molecular Discovery Ltd: http://www.moldiscovery.com (Accessed July 12, 2012).
62. Molegro ApS: http://molegro.com (Accessed July 12, 2012).
63. Osthus GmbH: http://products.osthus.de/en/workspace-module/ knimeintegration/ (Accessed July 12, 2012).
64. Schrödinger: http://www.schrodinger.com (Accessed July 12, 2012).
65. Accelrys: http://accelrys.com/products/informatics/chemin formatics/accelrys-cheshire.html (Accessed July 12, 2012).
66. Talete SRL: http://talete.mi.it (Accessed July 12, 2012).
67. Treweren Consultants Ltd: http://www.treweren.com (Accessed July 12, 2012).
68. Tripos: http://www.tripos.com (Accessed July 12, 2012).
69. Hudson, T.C.; Stapleton, A.E.; Brown, J.L. Codifying bioinformatics processes without programming. Drug Discov. Today Biosilico, 2004, 2(4), 164-169.
70. Néron, B.; Ménager, H.; Maufrai, SC.; Joly, N.; Maupetit, J.; Letort, S.; Carrere, S.; Tuffery, P.; Letondal, C. Mobyle: a new full web bioinformatics framework., Bioinformatics., 2009, 25(22), 3005-3011
71. Fiers, M.W.; van der Burgt, A.; Datema, E.; de Groot, J.C.; van Ham, R.C. High-throughput bioinformatics with the Cyrille2 pipe-line system. BMC Bioinformatics., 2008, 9, 96.
72. Giardine, B.; Riemer, C.; Hardison, R.C.; Burhans, R.; Elnitski, L.; Shah, P.; Zhang, Y.; Blankenberg, D.; Albert, I.; Taylor, J.; Miller, W.; Kent, W.J.; Nekrutenko, A. Galaxy: a platform for interactive large-scale genome analysis. Genome Res., 2005, 15(10), 1451-5.
73. KNime4Bio. http://code.google.com/p/knime4bio/ (Accessed July 12, 2012).
74. NCBI, National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov/ (Accessed July 12, 2012).
75. Thorvaldsdóttir, H., Robinson, J.T. Mesirov, J.P. Integrative Genomics Viewer (IGV): high-performance genomics data visualisation and exploration. Brief Bioinform., 2012, Epub ahead of print.
76. Sandberg, M.; Eriksson, L.; Jonsson, J.; Sjöström, M.; Wold, S. New Chemical Descriptors Relevant for the Design of Biologically Active Peptides. A Multivariate Characterisation of 87 Amino Acids. J. Med. Chem., 1998, 41(14), 2481-2491.
77. Banerjee, A.K.; Sunita, M.; Naveen, M.; Murty, U.S. Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties. Bioinformation, 2010, 4(10), 456-462.
78. Alladi, S.M.; Shinde, S.P.; Ravi, V.; Murthy, U.S. Colon cancer prediction with genetic profiles using intelligent techniques. Bioinformation, 2008, 3(2), 130-133.
79. PubChem Substance Database: http://www.ncbi.nlm.nih.gov/ pcsubstance/ (Accessed July 12, 2012).
80. PubChem BioAssay Database: http://www.ncbi.nlm.nih.gov/pcassay/ (Accessed July 12, 2012).
81. ChEMBL database, European Bioinformatics Institute: https://www.ebi.ac.uk/chembl/ (Accessed July 12, 2012).
82. Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K.; Mak, C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A.C.; Wishart, D.S. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res., 2011, D1035-D1041.
83. KEGG: Kyoto Encyclopedia of Genes and Genomes: http://www.genome.jp/kegg/ (Accessed July 12, 2012).
84. Extensible Markup Language (XML): http://www.w3.org/XML/ (Accessed July 12, 2012).
85. OWL 2 Web Ontology Language: http://www.w3.org/TR/owl2-overview/ (Accessed July 12, 2012).
86. Resource Description Framework (RDF): http://www.w3.org/RDF/ (Accessed July 12, 2012).
87. W3C Semantic Web: http://www.w3.org/2001/sw/ (Accessed July 12, 2012).
88. D.J. Mining large heterogeneous data sets in drug discovery. Expert Opin. Drug Discov., 2009, 4(10), 995-1004.
89. Zhu, Q.; Sun, Y.; Challa, S.; Ding, Y.; Lajiness, M.S.; Wild, D.J. Semantic inference using chemogenomics data for drug discovery. BMC Bioinformatics, 2011, 12, 256-267.
90. Williams, A.J.; Tkachenko, V.; Golotvin, S.; Kidd, R.; McCann, G. ChemSpider -building a foundation for the semantic web by hosting a crowd sourced databasing platform for chemistry. J. Cheminform., 2010; 2(Suppl 1): O16.
91. KNIME Network Visualization: http://tech.knime.org/book/network-visualization (Accessed July 15, 2012).
92. Haney, S.; LaPan, P.; Pan, J.; Zhang, J. High-content screening moves to the front of the line. Drug Discov. Today, 2006, 11(19-20), 889-894.
93. Zanella, F.; Lorens, J.B.; Link, W. High content screening: seeing is believing. Trends Biotechnol., 2010, 28(5), 237-245.
94. Harper, G.; Pickett, S.D. Methods for mining HTS data. Drug Discov. Today, 2006, 11(15/16), 694-699.
95. Kozak, K.; Agrawal, A.; Machuy, N.; Csucs, G. Data mining techniques in high content screening: A survey. J. Comput. Sci. Syst. Biol, 2009, 2(4), 219-239.
96. Strobelt, H.; Bertini, E.; Braun, J.; Deussen, O.; Groth, U.; Mayer, T.U.; Merhof, D. HiTSEE KNIME: a visualisation tool for hit selection and analysis in high-throughput screening experiments for the KNIME platform. BMC Bioinformatics, 2012, 13(Suppl 8), S4-S16.
97. Boutros, M.; Brás, L.; Huber, W. Analysis of cell-based RNAi screens. Genome Biol, 2006, 7(R66).
98. Mohamed, B.M.; Verma, N.K.; Prina-Mello, A.; Williams, Y.; Davies, A.M.; Bakos, G.; Tormey, L.; Edwards, C; Hanrahan, J.; Salvati, A.; Lynch, I.; Dawson, K.; Kelleher, D.; Volkov, Y. Activation of stress-related signalling pathway in human cells upon SiO2 nanoparticles exposure as an early indicator of cytotoxicity. J. Nanobiotechnology, 2011, 9(29)
99. Movia, D.; Prina-Mello, A.; Bazou, D.; Volkov, Y.; Giordani, S. Screening the cytotoxicity of single-walled carbon nanotubes using novel 3D tissue-mimetic models. ACS Nano., 2011, 5(11), 9278-9290.
100. Subramaniam, S.; Mehrotra, M.; Gupta, D. Virtual high throughput screening (vHTS) -A perspective. Bioinformation, 2008, 3(1), 14-17.
101. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res., 2000, 28(1), 235-242.
102. Protein Data Bank Japan: http://www.pdbj.org/doc/help.cgi?Search (Accessed July 12, 2012).
103. Kruggel, S.; Lemcke, T. Generation and evaluation of a homology model of PfGSK3. Arch. Pharm. Chem. Life Sci., 2009, 342, 327-332.
104. Korb, O.; ten Brink, T.; Raj, F.R.D.V.P.; Keil, M.; Exner, T.E. Are predefined decoy sets of ligand poses able to quantify scoring function accuracy? J. Comput. Aided Mol Des., 2012, 26, 185-197.
105. Sala, E.; Guasch, L.; Iwaszkiewicz, J.; Mulero, M.; Salvadó, M-J.; Pinent, M.; Zoete, V.; Grosdidier, A.; Garcia-Vallvé, S.; Michielin, O.; Pujadas, G.; Identification of Human IKK-2 Inhibitors of Natural Origin (Part I): Modeling of the IKK-2 Kinase Domain, Virtual Screening and Activity Assays. PLoS ONE, 2011, 6(2), e16903.
106. Motto, I.; Bordogna, A.; Soshilov, A.A.; Denison, M.S.; Bonati, L. New aryl hydrocarbon receptor homology model targets to improve docking reliability. J. Chem. Inf. Model, 2011, 51, 2868-2881.
107. Sadowski, J.; Gasteiger, J.; Klebe, G. J. J. Chem. Inf. Comput. Sci., 1994, 34, 1000-1008.
108. Bertini, S.; Asso, V.; Ghilardi, E.; Granchi, C; Manera, C; Minutolo, F.; Saccomanni, G.; Bortolato, A.; Mason, J.; Moro, S.; Macchia, M. Carbazole-containing arylcarboxamides as BACE1 inhibitors. Bioorg. Med. Chem. Lett., 2011, 21(22), 6657-61.
109. Geldenhuys, W.J.; Darvesh, A.S.; Funk, M.O.; Van der Schyf, C.J.; Carroll, R.T. Identification of novel monoamine oxidase B inhibitors by structure-based virtual screening. Bioorg. Med. Chem. Lett., 2010, 20(17), 5295-5298.
110. Moser, D.; Wisniewska, J.M.; Hahn, S.; Achenbach, J.; Buscató, E-L.; Klingler, F-M.; Hofmann, B.; Steinhilber, D.; Proschak, E. Dual-Target Virtual Screening by Pharmacophore Elucidation and Molecular Shape Filtering. ACS Med. Chem. Lett., 2012, 3(2), 155-158.
111. Vainio, M. J.; Puranen, J.S.; Johnson, M.S. ShaEP: Molecular Overlay Based on Shape and Electronic Potential. J. Chem. Inf. Model, 2009, 49, 492-502.
112. Baell, J.B.; Holloway, G. A., J. Med. Chem., 2010, 53, 2719-49.
113. Saubern, S.; Guha, R.; Baell, J.B. KNIME Workflow to assess PAINS filters in SMARTS format. Comparison of RDKit and Indigo cheminformatics libraries. Mol. Inf., 2011, 30, 847-850.
114. Irwin, J.J.; Shoichet, B.K. ZINC -A Free Database of Commercially Available Compounds for Virtual Screening. J. Chem. Inf. Model,. 2005, 45(1), 177-182.
115. Steri, R.; Achenbach, J.; Steinhilber, D.; Schubert-Zsilavecz, M.; Proschak, E. Investigation of imatinib and other approved drugs as starting points for antidiabetic drug discovery with FXR modulating activity. Biochem. Pharmacol, 2012, 83(12), 1674-1681.
116. Hutt, O.E.; Saubern, S.; Winkler, D.A. Modeling the molecular basis for a4(31 integrin antagonism. Bioorg. Med. Chem., 2011, 19(19), 5903-5911.
117. R. Flexible web service infrastructure for the development and deployment of predictive models. J. Chem. Inf. Model, 2008, 48, 456-464.
118. Dong, X.; Gilbert, K.E.; Guha, R.; Heiland, R.; Kim, J.; Pierce, M.E.; Fox, G.C.; Wild, D.J. Web service infrastructure for cheminformatics. J. Chem. Inf. Model, 2007, 47, 1303-1307.
119. Meinl, T.; Ostermann, C; Berthold, M.R. Maximum-score diversity selection for early drug discovery. J. Chem. Inf. Model, 2011, 51, 237-247.
120. Borgelt, C; Berthold, M.R.; Patterson, D.E. Molecular Fragment Mining for Drug Discovery. ECSQARU, 2005, 1002-1013.
121. Patel, H.; Bodkin, M.J.; Chen, B.; Gillet, V.J. Knowledge-based approach to de novo design using reaction vectors. J. Chem. Inf. Model, 2009, 49, 1163-1184.
122. Taylor, K.T. The status of electronic laboratory notebooks for chemistry and biology. Curr. Opin. Drug Discovery Dev., 2006, 9, 348-353.
123. Kirsten, ROW: reaction orientated workflows. Manuscript in preparation.
124. Roughley, S.D.; Jordan, A.M. The medicinal chemist's toolbox: An analysis of reactions used in the pursuit of drug candidates. J. Med. Chem., 2011, 54, 3451-3479.
125. CRAN Package QSARdata: http://cran.r-project.org/web/packages/ QSARdata/index.html (Accessed July 12, 2012).
126. Maunz, A.; Helma, C. In: Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery; Balakin, K.V, Ed.; John Wiley & Sons, Inc., Hoboken, NJ, USA: 2009, Chpt. 5, 145-173.
127. COSMOS: http://www.cosmostox.eu/what/knime/ (Accessed July 12, 2012).
128. Tonnelier, A.; Coecke, S.; Zaldívar, J-M. Screening of chemicals for human bioaccumulative potential with a physiologically based toxicokinetic model. Arch. Toxicol, 2012, 86(3), 393-403.
129. Enoch, S.J.; Cronin, M.T.D. Development of new structural alerts suitable for chemical category formation for assigning covalent and non-covalent mechanisms relevant to DNA binding. Mutat. Res., 2012, 743, 10-19.
130. Guha, R.; van Drie, J.H. Structure-Activity Landscape Index: Identifying and Quantifying Activity Cliffs. J. Chem. Inf. Model, 2008, 48(3), 646-658.
131. Bajorath, J.; Peltason, L.; Wawer, M.; Guha, R.; Lajiness, M.S.; and van Drie, J.H. Navigating structure-activity landscapes. Drug Discov. Today, 2009, 14(13), 698-705.
132. Guimarães, C.R.W.; Mathiowetz, A.M. Addressing limitations with the MM-GB/SA scoring procedure using the WaterMap method and free energy perturbation calculations. J. Chem. Inf. Model, 2010, 50, 547-559.
133. Culot, L.J. Data Pipelining with CambridgeSoft and KNIME: Essential to traditional and emerging discoveries. ChemBio News.Com., 20.2.
134. CRAN Task View: Analysis of Pharmacokinetic Data: http://cran.rproject.org/web/views/Pharmacokinetics.html (Accessed July 12, 2012).
135. Bioconductor version: Development (2.11) mzR package a parser for netCDF, mzXML, mzData and mzML files (mass spectrometry data): http://www.bioconductor.org/packages/devel/bioc/html/mzR.html (Accessed July 12, 2012).
136. Bioconductor version: Development (2.11) LC/MS and GC/MS Data Analysis: http://www.bioconductor.org/packages/release/bioc/ html/xcms.html (Accessed July 12, 2012).
137. CRAN Task View: Clinical Trial Design, Monitoring, and Analysis: http://cran.r-project.org/web/views/ClinicalTrials.html (Accessed July 12, 2012).
138. nd International Conference, Toulouse, France, August 29 -September 2, 2011; Hameurlain, A.; Liddle, S.W.; Schewe, K-D.; Zhou, X., Eds.; Springer-Verlag: Berlin, Heidelberg, Germany, 2011; pp. 59-73.
139. Silipo, R.; Vergassola, R.; Zong, W.; Berthold, M.R.; Knowledge-based and data-driven models in arrhythmia fuzzy classification. Methods Inf. Med., 2001, 40, 397-402.