Frequent itemsets for genomic profiling

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdragepeer review

2 Citaten (Scopus)


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.

Originele taal-2Engels
TitelComputational Life Sciences - First International Symposium, CompLife 2005, Proceedings
UitgeverijSpringer Verlag
Aantal pagina's13
ISBN van geprinte versie3540291040, 9783540291046
StatusGepubliceerd - 2005
Extern gepubliceerdJa
Evenement1st International Symposium on Computational Life Sciences, CompLife 2005 - Konstanz, Duitsland
Duur: 25 sep. 200527 sep. 2005

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3695 LNBI
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349


Congres1st International Symposium on Computational Life Sciences, CompLife 2005


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