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23, No. 1, January 2005. A Methodology for Analyzing SAGE Libraries for Cancer Profiling • 47 Fig. 3. Result of OPTICS after error removal, normalization, and missing tag imputation. The first kind of tags does not help in forming clustering structures. It even produces a dilution effect to the true clusters, by disturbing the similarity and dissimilarity of libraries. Thus, these tags are removed and only the discriminating tags are kept for further analysis. ACM Transactions on Information Systems, Vol.

In Proceedings of the Data Compression Conference. 253–262. , AND ASTOLA, J. 2003b. Classification and feature gene selection using the normalized maximum likelihood model for discrete regression. Signal Processing, Special Issue on Genomic Signal Processing 83, 4 (April), 713–727. WILBUR, W. AND LIPMAN, D. 1983. Rapid similarity searches of nucleic acid and protein data banks. In Proc. Natl. Acad. Sci. USA. Vol. 80. 726–730. WILLIAMS, H. AND ZOBEL, J. 1997. Compression of nucleotide databases for fast searching.

ACM Transactions on Information Systems, Vol. 23, No. 1, January 2005. An Efficient NML Algorithm for DNA Sequence Compression • 31 Table V. 76 Table VI. 3 Human Genome Compression In order to illustrate the practical strength of our algorithm, we have compressed the complete human genome as released on April 14, 2003, taken from the NCBI web site. Apart from symbols for the nucleotides A, C, G and T , these files, which are distributed in the FASTA format, regularly contain undetermined (possibly unknown or unstable during the sequencing process) bases denoted by the symbol N .

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ACM transactions on information systems (January) by Association for Computing Machinery


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