Motif discovery in physiological datasets: A methodology for inferring predictive elements

ACM Transactions on Knowledge Discovery from Data

Volumn 4, Issue 1, 2010, Pages

  Syed, Zeeshan ^a   Stultz, Collin ^b   Kellis, Manolis ^b   Indyk, Piotr ^b   Guttag, John ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Data mining; Gibbs sampling; Inference; Knowledge discovery; Motifs; Physiological signals

Indexed keywords

GIBBS SAMPLING; INFERENCE; KNOWLEDGE DISCOVERY; MOTIFS; PHYSIOLOGICAL SIGNALS;

COMPUTATIONAL EFFICIENCY; DATA MINING; GENES;

PHYSIOLOGY;

EID: 77955605527     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/1644873.1644875     Document Type: Article

References (24)

1. BAILEY, T. AND EKLAN, C. 1995. The value of prior knowledge in discovery motifs with MEME. In Proceedings of the International Conference on Intelligence Systems in Molecular Biology. 21-29.
2. CHIU, B., KEOGH, E., AND LONARDI, S. 2003. Probabilistic discovery of time series motifs. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
3. CESA-BIANCHI, N. AND LUGOSI, G. 2006. Prediction, Learning and Games. Cambridge University Press, Cambridge, UK.
4. CROOKS, G., HON, G., CHANDONIA, J., AND BRENNER, S. 2004. WebLogo: a sequence long generator. Genome Res. 14, 1188-1190.
5. DAW, C., FINNEY C., AND TRACY, E. 2003. A review of symbolic analysis of experimental data. Rev. Sci. Instr. 74, 915-930.
6. DURBIN, R., EDDY, S., KROGH, A., ANDMITCHISON, G. 1998. Biological Sequence Analysis. Cambridge University Press, Cambridge,UK.
7. FREITAS, A. 2001. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag, Berlin, Germany.
8. GERT, T., MARCHAL, K., LESCOT, M., ROMBAUTS, S., DE MOOR, B., ROUZE, P., AND MOREAU Y. 2002. A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes. J. Comput. Biol. 9, 447-464.
9. GILES, C. AND LAWRENCE, S. 2001. Noisy time series prediction using a recurrent neural network and grammatical inference. Mach. Learn. 44, 161-183.
10. GIONIS, A., MANNILA, H., MIELIKAINEN, T., AND TSAPARAS, P. 2006. Assessing data mining results via swap randomization. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
11. GOLDBERGER, A., AMARAL, L., GLASS, L., HAUSDORFF, J., INVANOV, P., MARK, R., MIETUS, J., MOODY, G., PENG, C., AND STANLEY,H. 2000. Components of a new research resource for complex physiologic signals. Circulation 101, 215-220.
12. HARMS, S., DEOGUN, J. AND TADESSE, T. 2002. Discovering sequential association rules with constraints and time lags in multiple sequences. In Proceedings of the 13th International Symposium on Foundation of Intelligent Systems.
13. HELMBOLD, D. AND SCHAPIRE, R. 1997. Predicting nearly as well as the best pruning of a decision tree. Mach. Learn. 27, 51-68.
14. JIN, X., WANG, L., LU, Y., AND SHI, C. 2002. Indexing and mining of the local patterns in sequence database. In Proceedings of the 3rd International Conference on Intelligent Data Engineering and Automated Learning.
15. JONES, N. AND PEVZNER, P. 2004. An Introduction to Bioinformatics Algorithms. The MIT Press. Cambridge, MA.
16. KELLIS, M., PATTERSON, N., ENDRIZZI, M., BIRREN, B., AND LANDER, E. 2003. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423, 241-254.
17. LIN, J., KEOGH, E., LONARDI, S., AND PATEL, P. 2002. Finding motifs in time series. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
18. LIN, J., KEOGH, E., LONARDI, S., AND CHIU, B. 2003. A symbolic representation of time series with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.
19. MANNILA, H., TOIVONEN, H., AND VERKAMO, A. 1997. Discovery of frequent episodes in event sequences. Data Mining and Knowl. Discov. 1, 259-289. (Pubitemid 127721244)
20. MANNILA, H., PAVLOV, D., AND SMYTH, P. 1999. Prediction with local patterns using cross-entropy. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
21. PATEL, P.,KEOGH, E., LIN, J., AND LONARDI, S. 2002. Mining motifs in massive time series databases. In Proceedings of the International Conference on Data Mining.
22. SCHOLKOPF, B., PLATT, J., SHAWE-TAYLOR, J., SMOLA, A., AND WILLIAMSON, R. 2001. Estimating the support of a high-dimensional distribution. Neural Comput. 13, 1443-1471.
23. STORMO, G. AND HARTZELL, G. 1989. Identifying protein-binding sites from unaligned DNA fragments. Proc. Nat. Acad. Sciences 86, 1183-1187.
24. SYED, Z., STULTZ, C., AND GUTTAG, J. 2007. Clustering and symbolic analysis of cardiovascular signals: Discovery and visualization of medically relevant patterns in long-term data using limited prior knowledge. EURASIP J. Advances Sig. Proc. 2007, Article ID 67938.