Association analysis techniques for bioinformatics problems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5462 LNBI, Issue , 2009, Pages 1-13

  Atluri, Gowtham ^a   Gupta, Rohit ^a   Fang, Gang ^a   Pandey, Gaurav ^a   Steinbach, Michael ^a   Kumar, Vipin ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Association Analysis; Bioinformatics; Data Mining; Frequent

Indexed keywords

ASSOCIATION ANALYSIS; ASSOCIATION PATTERNS; CLASSIFICATION AND CLUSTERING; COMPUTATIONAL BIOLOGY; FREQUENT; POTENTIAL APPLICATIONS; PRE-PROCESSING; PROTEIN FUNCTION PREDICTION; PROTEIN INTERACTION NETWORKS;

ASSOCIATION REACTIONS; BIOLOGY; MINING;

BIOINFORMATICS;

EID: 68249109591     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-00727-9_1     Document Type: Conference Paper

References (64)

1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proc. SIGMOD, pp. 207-216 (1993)
2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. VLDB, pp. 487-499 (1994)
3. Balding, D.: A tutorial on statistical methods for population association studies. Nature Reviews Genetics 7(10), 781 (2006)
4. Bay, S., Pazzani, M.: Detecting group differences: Mining contrast sets. DMKD 5(3), 213-246 (2001)
5. Becquet, C., et al.: Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human sage data. Genome Biology 3 (2002)
6. Bergmann, S., Ihmels, J., Barkai, N.: Iterative signature algorithm for the analysis of large-scale gene expression data. Physical Review 67 (2003)
7. Breitkreutz, B.-J., Stark, C., Tyers, M.: The GRID: the General Repository for Interaction Datasets. Genome Biology 4(3), R23 (2003)
8. Ceglar, A., Roddick, J.F.: Association mining. ACM Comput. Surv. 38(2), 5 (2006)
9. Cheng, H., Yan, X., Han, J., Hsu, C.-W.: Discriminative frequent pattern analysis for effective classification. In: Proc. IEEE ICDE, pp. 716-725 (2007)
10. Cheng, H., Yan, X., Han, J., Yu, P.: Direct mining of discriminative and essential graphical and itemset features via model-based search tree. In: Proc. ACM SIGKDD International Conference, pp. 230-238 (2008)
11. Cheng, H., Yu, P.S., Han, J.: Ac-close: Efficiently mining approximate closed itemsets by core pattern recovery. In: Proceedings of the 2006 IEEE International Conference on Data Mining, pp. 839-844 (2006)
12. Cheng, Y., Church, G.: Biclustering of Expression Data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology table of contents, pp. 93-103. AAAI Press, Menlo Park (2000)
13. Creighton, C., Hanash, S.: Mining gene expression databases for association rules. Bioinformatics 19(1), 79-86 (2003)
14. Deane, C.M., Salwinski, L., Xenarios, I., Eisenberg, D.: Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics 1(5), 349-356 (2002)
15. Deng, M., Sun, F., Chen, T.: Assessment of the reliability of protein-protein interactions and protein function prediction. In: Pac. Symp. Biocomputing, pp. 140-151 (2003)
16. Dong, G., Li, J.: Efficient mining of emerging paterns: Discovering trends and differences. In: Proceedings of the 2001 ACM SIGKDD International Conference, pp. 43-52 (1999)
17. Eisenberg, D., Marcotte, E.M., Xenarios, I., Yeates, T.O.: Protein function in the post-genomic era. Nature 405(6788), 823-826 (2000)
18. Fan, W., Zhang, K., Cheng, H., Gao, J., Yan, X., Han, J., Yu, P.S., Verscheure, O.: Direct discriminative pattern mining for effective classification. In: Proc. IEEE ICDE, pp. 169-178 (2008)
19. Gupta, R., Fang, G., Field, B., Steinbach, M., Kumar, V.: Quantitative evaluation of approximate frequent pattern mining algorithms. In: Proceeding of the 14th ACM SIGKDD Conference, pp. 301-309 (2008)
20. Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery 8(1), 53-87 (2004)
21. Hart, G.T., Ramani, A.K., Marcotte, E.M.: How complete are current yeast and human protein-interaction networks? Genome. Biol. 7(11), 120 (2006)
22. Hirschhorn, J.: Genetic Approaches to Studying Common Diseases and Complex Traits. Pediatric Research 57(5 Part 2), 74R (2005)
23. Klemettinen, M., Mannila, H., Toivonen, H.: Rule Discovery in Telecommunication Alarm Data. J. Network and Systems Management 7(4), 395-423 (1999)
24. Kuramochi, M., Karypis, G.: An efficient algorithm for discovering frequent subgraphs. IEEE Trans. on Knowl. and Data Eng. 16(9), 1038-1051 (2004)
25. Legrain, P., Wojcik, J., Gauthier, J.-M.: Protein-protein interaction maps: a lead towards cellular functions. Trends Genet. 17(6), 346-352 (2001)
26. Lin, C., Jiang, D., Zhang, A.: Prediction of protein function using commonneighbors in protein-protein interaction networks. In: Proc. IEEE Symposium on BionInformatics and BioEngineering (BIBE), pp. 251-260 (2006)
27. Liu, J., Paulsen, S., Sun, X., Wang, W., Nobel, A., Prins, J.: Mining Approximate Frequent Itemsets In the Presence of Noise: Algorithm and Analysis. In: Proc. SIAM International Conference on Data Mining (2006)
28. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinf. 1(1), 24-45 (2004)
29. Martinez, R., Pasquier, N., Pasquier, C.: GenMiner: mining non-redundant association rules from integrated gene expression data and annotations. Bioinformatics 24(22), 2643-2644 (2008)
30. McIntosh, T., Chawla, S.: High confidence rule mining for microarray analysis. IEEE/ACM Trans. Comput. Biol. Bioinf. 4(4), 611-623 (2007)
31. Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., Singh, M.: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21(suppl. 1), i1-i9 (2005)
32. Nelson, M., Kardia, S., Ferrell, R., Sing, C.: A Combinatorial Partitioning Method to Identify Multilocus Genotypic Partitions That Predict Quantitative Trait Variation. Genome Research 11(3), 458-470 (2001)
33. Nguyen, D.V., Arpat, A.B., Wang, N., Carroll, R.J.: DNA microarray experiments: biological and technological aspects. Biometrics 58(4), 701-717 (2002)
34. Pandey, G., Atluri, G., Steinbach, M., Kumar, V.: Association analysis for realvalued data: Definitions and application to microarray data. Technical Report 08-1007, Department of Computer Science and Engineering, University of Minnesota (March 2008)
35. Pandey, G., Atluri, G., Steinbach, M., Kumar, V.: Association analysis techniques for discovering functional modules from microarray data. Nature Proceedings, Presented at ISMB, SIG Meeting on Automated Function Prediction (2008), http://dx.doi.org/10.1038/npre.2008.2184.1
36. Pandey, G., Kumar, V., Steinbach, M.: Computational approaches for protein function prediction: A survey. Technical Report 06-028, Department of Computer Science and Engineering, University of Minnesota (October 2006)
37. Pandey, G., Steinbach, M., Gupta, R., Garg, T., Kumar, V.: Association analysisbased transformations for protein interaction networks: a function prediction case study. In: Proceedings of the 13th ACM SIGKDD International Conference, pp. 540-549 (2007)
38. Pei, J., Tung, A., Han, J.: Fault-tolerant frequent pattern mining: Problems and challenges. In: Workshop on Research Issues in Data Mining and Knowledge Discovery (2001)
39. Pereira-Leal, J.B., Enright, A.J., Ouzounis, C.A.: Detection of functional modules from protein interaction networks. Proteins 54(1), 49-57 (2003)
40. Pfaltz, J., Taylor, C.: Closed set mining of biological data. In: Workshop on Data Mining in Bioinformatics (BIOKDD) (2002)
41. Pu, S., Ronen, K., Vlasblom, J., Greenblatt, J., Wodak, S.J.: Local coherence in genetic interaction patterns reveals prevalent functional versatility. Bioinformatics 24(20), 2376-2383 (2008)
42. Ritchie, M., et al.: Multifactordimensionality reduction reveals high-order iteractions among estrogen- metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69(1), 1245-1250 (2001)
43. Ruepp, A., et al.: The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res. 32(18), 5539-5545 (2004)
44. Salwinski, L., Eisenberg, D.: Computational methods of analysis of protein-protein interactions. Curr. Opin. Struct. Biology 13(3), 377-382 (2003)
45. Samanta, M.P., Liang, S.: Predicting protein functions from redundancies in largescale protein interaction networks. Proc. Natl. Acad Sci. U.S.A. 100(22), 12579-12583 (2003)
46. Schwikowski, B., Uetz, P., Fields, S.: A network of protein-protein interactions in yeast. Nature Biotechnology 18(12), 1257-1261 (2000)
47. Seppanen, J., Mannila, H.: Dense itemsets. In: KDD, pp. 683-688 (2004)
48. Seshasayee, A.S.N., Babu, M.M.: Contextual inference of protein function. In: Subramaniam, S. (ed.) Encyclopaedia of Genetics and Genomics and Proteomics and Bioinformatics. John Wiley and Sons, Chichester (2005)
49. Tan, P., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of the eighth ACM SIGKDD International Conference, pp. 32-41 (2002)
50. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison- Wesley, Reading (2005)
51. Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(suppl. 1), 136-144 (2002)
52. Tramontano, A.: The Ten Most Wanted Solutions in Protein Bioinformatics. CRC Press, Boca Raton (2005)
53. Van Vliet, M., Klijn, C., Wessels, L., Reinders, M.: Module-based outcome prediction using breast cancer compendia. PLoS ONE 2(10), 1047 (2007)
54. Vazquez, A., Flammini, A., Maritan, A., Vespignani, A.: Global protein function prediction from protein-protein interaction networks. Nat. Biotechnology 21(6), 697-700 (2003)
55. Wang, J., Karypis, G.: Harmony: Efficiently mining the best rules for classification. In: Proceedings of SIAM International Conference on Data Mining, pp. 205-216 (2005)
56. Xenarios, I., Salwinski, L., Duan, X.J., Higney, P., Kim, S.-M., Eisenberg, D.: DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research 30(1), 303-305 (2002)
57. Xiong, H., He, X., Ding, C., Zhang, Y., Kumar, V., Holbrook, S.R.: Identification of functional modules in protein complexes via hyperclique pattern discovery. In: Proc. Pacific Symposium on Biocomputing (PSB), pp. 221-232 (2005)
58. Xiong, H., Pandey, G., Steinbach, M., Kumar, V.: Enhancing data analysis with noise removal. IEEE Trans. on Knowl. and Data Eng. 18(3), 304-319 (2006)
59. Xiong, H., Steinbach, M., Kumar, V.: Privacy leakage in multi-relational databases via pattern based semi-supervised learning. In: Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 355-356. ACM, New York (2005)
60. Xiong, H., Steinbach, M., Tan, P., Kumar, V.: HICAP: Hierarchial Clustering with Pattern Preservation. In: Proceedings of the 4th SIAM International Conference on Data Mining, pp. 279-290 (2004)
61. Xiong, H., Tan, P.-N., Kumar, V.: Hyperclique pattern discovery. Data Min. Knowl. Discov. 13(2), 219-242 (2006)
62. Yang, C., Fayyad, U., Bradley, P.: Efficient discovery of error-tolerant frequent itemsets in high dimensions. In: Proc. ACM SIGKDD, pp. 194-203 (2001)
63. Yona, G., Dirks, W., Rahman, S., Lin, D.M.: Effective similarity measures for expression profiles. Bioinformatics 22(13), 1616-1622 (2006)
64. Zaki, M., Ogihara, M.: Theoretical foundations of association rules. In: 3rd ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (June 1998)