Frequent Itemsets for Genomic Profiling

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Frequent Itemsets for Genomic Profiling

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by Jeannette M. De Graaf , Renée X. De Menezes , Judith M. Boer , Walter A. Kosters

Venue:	In Proc. 1st International Symposium on Computational Life Sciences (CompLife 2005), LNCS 3695
Citations:	6 - 3 self

BibTeX

@INPROCEEDINGS{Graaf_frequentitemsets,
    author = {Jeannette M. De Graaf and Renée X. De Menezes and Judith M. Boer and Walter A. Kosters},
    title = {Frequent Itemsets for Genomic Profiling},
    booktitle = {In Proc. 1st International Symposium on Computational Life Sciences (CompLife 2005), LNCS 3695},
    year = {},
    pages = {104--116},
    publisher = {Springer}
}

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Abstract

Abstract. Frequent itemset mining is a promising approach to the study of genomic profiling data. Here a dataset consists of real numbers describing the relative level in which a clone occurs in human DNA for given patient samples. One can then mine, for example, for sets of samples that share some common behavior on the clones, i.e., gains or losses. Frequent itemsets show promising biological expressiveness, can be computed efficiently, and are very flexible. Their visualization provides the biologist with useful information for the discovery of patterns. Also it turns out that the use of (larger) frequent itemsets tends to filter out noise. 1

Keyphrases

frequent itemsets genomic profiling common behavior human dna promising biological expressiveness frequent itemset mining useful information promising approach relative level patient sample genomic profiling data real number

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