Scientific data analysis

Scientific Data Management: Challenges, Technology, and Deployment

Volumn , Issue , 2009, Pages 281-323

  Kamath, Chandrika ^a   Wale, Nikil ^b   Karypis, George ^b   Pandey, Gaurav ^b   Kumar, Vipin ^b   Rajan, Krishna ^c   Samatova, Nagiza F ^d,e   Breimyer, Paul ^d,e   Kora, Guruprasad ^d,e   Pan, Chongle ^d,e   Yoginath, Srikanth ^d,e  

^a LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c IOWA STATE UNIVERSITY   (United States)

^d OAK RIDGE NATIONAL LABORATORY   (United States)

^e NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 74949141251     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/9781420069815     Document Type: Chapter

References (96)

1. Kamath, C. Scientific Data Mining: A Practical Perspective. SIAM, Philadelphia, PA, 2009.
2. Leach, A. R., and Gillet, V. J. An Introduction to Chemoinformatics. Dordorecht, The Netherlands: Springer, 2003.
3. Southan, C., Varkonyi, P., and Muresan, S. Complementarity between public and commercial databases: new opportunities in medicinal chemistry informatics. Current Topics in Medicinal Chemistry 7, 15 (2007), 1502-1508.
4. Bohm, H., and Schneider, G. Virtual Screening for Bioactive Molecules. Weinheim, Germany Wiley-VCH, 2000.
5. Hansch, C., Maolney, P. P., Fujita, T., and Muir, R. M. Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature 194 (1962), 178-180.
6. Daylight Inc, Mission Viejo, CA, USA. http://www.daylight.com. Accessed July 12, 2009.
7. Pipeline project, Scitegic inc. http://www.scitegic.com. Accessed July 12, 2009.
8. Sybyl, Tripos inc. http://www.tripos.com/. Accessed July 12, 2009.
9. Screen, Chemaxon inc. http://www.chemaxon.com. Accessed July 12, 2009.
10. MDL Information Systems Inc., San Leandro, CA, USA. http://www.mdl.com. Accessed July 12, 2009.
11. Wale, N., Watson, I. A., and Karypis, G. Comparison of descriptor spaces for chemical compound retrieval and classification. Knowledge and Information Systems (in press) (2007).
12. King, R. D., Srinivasan, A., and Dehaspe, L. Warmr: A data mining tool for chemical data. Journal of Computer Aided Molecular Design 15 (2001), 173-181.
13. Ralaivola, L., Swamidassa, S. J., Saigo, H., and Baldi., P. Graph kernels for chemical informatics. Neural Networks 18, 8 (2005), 1093-1110.
14. Menchetti, S., Costa, F., and Frasconi, P. Weighted decomposition kernels. Proceedings of the 22nd International Conference in Machine Learning. 119 (2005), 585-592.
15. Raymond, J. W., and Willett, P. Maximum common subgraph isomorphism algorithms for the matching of chemical structures. J. Comp. Aided Mol. Des. 16, 7 (2002), 521-533.
16. Muller, K. R., Mika, S., Ratsch, G., Tsuda, K., and Scholkopf., B. An introduction to kernel-based learning algorithms. IEEE Trans. Neural. Net. 12, 2 (2001), 181-201.
17. Kuramochi, M., and Karypis, G. An efficient algorithm for discovering frequent subgraphs. IEEE TKDE. 16, 9 (2004.), 1038-1051.
18. Deshpande, M., Kuramochi, M., Wale, N., and Karypis., G. Frequent substructure-based approaches for classifying chemical compounds. IEEE TKDE. 17, 8 (2005), 1036-1050.
19. Horvath, T., Gartner, T., and Wrobel, S. Cyclic pattern kernels for predictive graph mining. Proceedings of the SIGKDD (2004), 158-167.
20. The Pubchem project. http://pubchem.ncbi.nlm.nih.gov. Accessed July 12, 2009.
21. Wale, N., Karypis, G., and Watson, I. A. Method for effective virtual screening and scaffold-hopping in chemical compounds. Comput Syst Bioinformatics Conf 6 (2007), 403-414.
22. Kuramochi, M., and Karypis, G. Discovering frequent geometric subgraphs. Information Systems 32, 8 (2007), 1101-1120.
23. Paolini, G. V., Shapland, R. H., Hoorn, W. P. V., Mason, J. S., and Hopkins., A. Global mapping of pharmacological space. Nature Biotechnology 24 (2006), 805-815.
24. Russ, A. P., and Lampel, S. The druggable genome: an update. Drug Discov Today 10, 23-24 (2005), 1607-1610.
25. Yildirim, M. A., Goh, K.-I., Cusick, M. E., Barabási, A.-L., and Vidal, M. Drug-target network. Nat Biotechnol 25, 10 (Oct 2007), 1119-1126.
26. Rognan, D. Chemogenomic approaches to rational drug design. Br J Pharmacol 152, 1 (Sep 2007), 38-52.
27. Stockwell, B. R. Exploring biology with small organic molecules. Nature 432, 7019 (Dec 2004), 846-854.
28. Jenkins, J. L., Bender, A., and Davies, J. W. In silico target fishing: Predicting biological targets from chemical structure. Drug Discovery Today 3, 4 (2006), 413-421.
29. Oliver, S. A network approach to the systematic analysis of yeast gene function. Trends in Genetics 12, 7 (1996), 241-242.
30. EUROFAN 2000: The Final Meeting (2000).
31. Roberts, R. J. Identifying protein function, a call for community action. PLoS Biology 2, 3 (2004), 293-294.
32. Meyer, F. Genome sequencing vs. Moore’s law: Cyber challenges for the next decade. CTWatch Quarterly, August 2006.
33. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.-C., Estreicher, A., et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Research 31, 1 (2003), 365-370.
34. Mewes, H. W., Frishman, D., Guldener, U., Mannhaupt, G., Mayer, K., et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Research 30, 1 (2002), 31-34.
35. Xenarios, I., Salwinski, L., Duan, X. J., Higney, P., Kim, S.-M., and Eisenberg, D. DIP and the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research 30, 1 (2002), 303-305.
36. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., and Bourne, P. E. The protein data bank. Nucleic Acids Research 28, 1 (2000), 235-242.
37. Rastan, S., and Beeley, L. J. Functional genomics: going forwards from the databases. Curr Opin Genet Dev. 7, 6 (1997), 777-783.
38. Altschul, S. F., Gish, W., Miller, W., Meyers, E. W., and Lipman, D. J. Basic local alignment search tool. J Mol Biol. 215, 3 (1990), 403-410.
39. Altschul, S. F., Madden, T. L., Schiffer, A. A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D. J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25, 17 (1997), 3389-3402.
40. Devos, D., and Valencia, A. Practical limits of function prediction. Proteins 41, 1 (2000), 98-107.
41. Whisstock, J. C., and Lesk, A. M. Prediction of protein function from protein sequence and structure. Q Rev Biophys. 36, 3 (2003), 307-340.
42. Pandey, G., Kumar, V., and Steinbach, M. Computational approaches for protein function prediction: A survey. Tech. Rep. 06-028, Department of Computer Science and Engineering, University of Minnesota, Twin Cities, 2006.
43. Bork, P., Dandekar, T., Diaz-Lazcoz, Y., Eisenhaber, F., Huynen, M., and Yuan, Y. Predicting function: from genes to genomes and back. J Mol Biol. 283, 4 (1998), 707-725.
44. Rost, B., Liu, J., Nair, R., Wrzeszczynski, K. O., and Ofran, Y. Automatic prediction of protein function. Cell Mol Life Sci. 60, 12 (2003), 2637-2650.
45. Gabaldon, T., and Huynen, M. A. Prediction of protein function and pathways in the genome era. Cell Mol Life Sci. 61, 7-8 (2004), 930-944.
46. Seshasayee, A. S. N., and Babu, M. M. Contextual inference of protein function. In Encyclopaedia of Genetics and Genomics and Proteomics and Bioinformatics, M. J. Dunn, L. B. Jorde, P. F. R. Little, and S. Subramaniam (Eds.), Hoboken, NJ: John Wiley and Sons, 2005.
47. Tan, P.-N., Steinbach, M., and Kumar, V. Introduction to Data Mining. Pearson Addison-Wesley, 2006.
48. Huynen, M. A., Snel, B., Bork, P., and Gibson, T. J. The phylogenetic distribution of frataxin indicates a role in iron-sulfur cluster protein assembly. Hum Mol Genet. 10, 21 (2001), 2463-2468.
49. Myers, C. L., Robson, D., Wible, A., Hibbs, M. A., Chiriac, C., et al. Discovery of biological networks from diverse functional genomic data. Genome Biology 6 (2005), R114.
50. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., et al. Gene ontology: tool for the unification of biology. Nature Genetics 25, 1 (2000), 25-29.
51. Brusic, V., and Zeleznikow, J. Knowledge discovery and data mining in biological databases. Knowl. Eng. Rev. 14, 3 (1999), 257-277.
52. Hart, G. T., Ramani, A. K., and Marcotte, E. M. How complete are current yeast and human protein-interaction networks? Genome Biology 7, 11 (2006), 120.
53. Deane, C. M., Salwinski, L., Xenarios, I., and Eisenberg, D. Protein interactions: Two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics 1, 5 (2002), 349-356.
54. Brun, C., Chevenet, F., Martin, D., Wojcik, J., Guenoche, A., and Jacq, B. Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biology 5, 1 (2003), R6.
55. Samanta, M. P., and Liang, S. Predicting protein functions from redundancies in large-scale protein interaction networks. Proc Natl Acad Sci U.S.A. 100, 22 (2003), 12579-12583.
56. Chen, J., Chua, H. N., Hsu, W., Lee, M.-L., Ng, S.-K., Saito, R., Sung, W.-K., and Wong, L. Increasing confidence of protein-protein inteactomes. In Proceedings of 17th International Conference on Genome Informatics (GIW) (2006), pp. 284-297.
57. Pandey, G., Steinbach, M., Gupta, R., Garg, T., and Kumar, V. Association analysis-based transformations for protein interaction networks: A function prediction case study. In KDD '07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2007), pp. 540-549.
58. Xiong, H., Tan, P.-N., and Kumar, V. Hyperclique pattern discovery. Data Min. Knowl. Discov. 13, 2 (2006), 219-242.
59. Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., and Singh, M. Wholeproteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21, Suppl. 1 (2005), i1-i9.
60. Xiong, H., Pandey, G., Steinbach, M., and Kumar, V. Enhancing data analysis with noise removal. IEEE Transactions on Knowledge and Data Engineering 18, 3 (2006), 304-319.
61. Smyth, G. K., and Speed, T. Normalization of cDNA microarray data. Methods 31, 4 (2003), 265-273.
62. Grimvall, G. Thermophysical Properties of Materials. Elsevier, Amsterdam, 1999.
63. Ledbetter, H., and Kim, S. Bulk moduli systematics in oxides, including superconductors. In Handbook of Elastic Properties of Solids, Liquids and Gases, H. B. M. Levy and R. R. Stern, Eds. San Diego, CA: Academic Press, 2000.
64. Iwata, S., et al. Materials database for materials design. Journal of Nuclear Materials 179-181 (1992), 1135.
65. Davis, J. W. Development of an international fusion materials database. Journal of Nuclear Materials 179-181 (1992), 1139.
66. Saxena, S., et al. Thermodynamic Data on Oxides and Silicates. Springer, New York, 1993, page 428.
67. Fabrichnaya, O. B., and Sundman, B. The assessment of thermodynamic parameters in the Fe-O and Fe-Si-O systems. Geochimica et Cosmochimica ACTA 61, 21 (1997), 4539-4555.
68. Fabrichnaya, O. B., et al. Thermodynamic Data, Models, and Phase Diagrams in Multicomponent Oxide Systems. Springer, New York, 2003.
69. Bale, C. W., et al. Factsage thermochemical software and databases. Calphad 26, 2 (2002), 189-228.
70. Cox, J., et al., Eds. CODATA Key Values for Thermodynamics. Hemisphere Publishing Corp., New York, 1989.
71. Soligo, D., et al. Non-generic concentrations for shape-memory alloys; the case of CuZnAl. Acta Materialia 47 (1999), 2741.
72. Villars, P., et al. Binary, ternary and quaternary compound former/nonformer prediction via mendeleev number. Journal of Alloys and Compounds 317-318 (2001), 26.
73. Ashby, M. F. Materials Selection in Mechanical Design. Butterworth-Heinemann, Oxford, 1999.
74. Shercliff, H. R., and Lsvatt, A. M. Selection of manufacturing processes in design and the role of process modeling. Progress in Materials Science 46 (2001), 429-459.
75. Lovatt, A. M., and Shercliff, H. R. Manufacturing process selection in engineering design. Materials and Design 19 (1998), 205-230.
76. Ashby, M. F. Checks and estimates for materials properties: I. ranges and simple correlations. Proc Royal Soc. London A 454, 1873 (1998), 1301-1321.
77. Bassetti, D., et al. Checks and estimates for materials properties: I. the method of multiple correlations. Proc Royal Soc. London A 454, 1873 (1998), 1323-1336.
78. Zaki, M., and Rajan, K. Data mining: a tool for materials discovery. In Proceedings of 17th CODATA meeting (2002). http://www.cs.rpi.edu/zaki/ps/codata.00.ps.gz. Accessed on July 12, 2009.
79. Bajorath, J. Integration of virtual and high-throughput screening. Nature Reviews Drug Discovery 1 (2002), 882-894.
80. Quakenbush, J. Compuational analysis of microarray data. Nature Reviews Genetics 2 (2001), 418-427.
81. Li, G., et al. High dimensional model representations. J. Physical Chemistry 104 (2001), 7765.
82. Rajan, K., Suh, C., and Narasimhan, B. Informatics methods for combinatorial materials science. In Combinatorial Materials Science, S. Mallapragada, B. Narasimhan, and M. Porter (Eds.), John Wiley & Sons, New York, 2007.
83. Suh, C., and Rajan, K. Invited review: Data mining and information for crystal chemistry: Establishing measurement techniques for mapping structure-property relationships. J. Materials Science and Technology Volume 25(4) April 2009 pp. 466-471.
84. Willars, P., and Phillips, J. C. Quantum structural diagrams and high Tc superconductivity. Phys. Rev Letters 37 (1988), 2345-2348.
85. Suh, C., and Rajan, K. Virtual screening and QSAR formulations for crystal chemistry. QSAR & Combinatorial Science Journal 24 (2005), 114.
86. Rajagopalan, A., and Rajan, K. Informatics based optimization of crystallographic descriptors for framework structures. In Combinatorial and High Throughput Discovery and Optimization of Catalysts and Materials, R. A. Potyrailo and W. F. Maier (Eds.), CRC Press, Boca Raton, FL, 2006.
87. Rajagopalan, A., et al. Secondary descriptor development for zeolite framework design: an informatics approach. Applied Catalysis A 254 (2003), 147-160.
88. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2005. ISBN 3-900051-07-0.
89. Yu, H. The Rmpi Package. R Foundation for Statistical Computing, Vienna, Austria, 2006.
90. Li N., and Rossini, A. J. The rpvm Package. R Foundation for Statistical Computing, Vienna, Austria, 2005.
91. Tierney, L., Rossini, A. J., Li, N., and Sevcikova, H. The Snow Package. R Foundation for Statistical Computing, Vienna, Austria, 2006.
92. Yoginath, S., Samatova, N., Bauer, D., Kora, G., Fann, G., and Geist, A. RScaLAPACK: High performance parallel statistical computing with R and ScaLAPACK. In Proceedings of the 18th International Conference on Parallel and Distributed Computing Systems (2005), pp. 61-67.
93. Samatova, N., et al. High performance statistical computing with parallel R: applications to biology and climate modeling. Journal of Physics: Conference Series, 46 (2006).
94. L. S. Blackford, J. Choi, A. Cleary, E. D’Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, and R. C. Whaley. ScaLAPACK User’s Guide, 1997.
95. Pan, C., Kora, G., Tabb, D. L., Pelletier, D. A., McDonald, W. H., Hurst, G. B., Hettich, R. L. and Samatova, N. F. Robust estimation of peptide abundance ratios and rigorous scoring of their variability and bias in quantitative shotgun proteomics. Analytical Chemistry 78(20) (2006), 7110-7120.
96. Pan, C., Kora, G., McDonald, W. H., Tabb, D. L., VerBerkmoes, N. C., Hurst, G. B., Pelletier, D. A., Samatova, N. F., and Hettich, R. L. Prorata: a quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval evaluation. Analytical Chemistry 78(20) (2006), 7121-7131.