Frequent itemsets for genomic profiling

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

doi:10.1007/11560500_10

Frequent itemsets for genomic profiling

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings
Publisher	Springer Verlag
Pages	104-116
Number of pages	13
ISBN (Print)	3540291040, 9783540291046
DOIs	https://doi.org/10.1007/11560500_10
Publication status	Published - 2005
Externally published	Yes
Event	1st International Symposium on Computational Life Sciences, CompLife 2005 - Konstanz, Germany Duration: 25 Sept 2005 → 27 Sept 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3695 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Symposium on Computational Life Sciences, CompLife 2005
Country/Territory	Germany
City	Konstanz
Period	25/09/05 → 27/09/05

Access to Document

10.1007/11560500_10

Cite this

De Graaf, J. M., De Menezes, R. X., Boer, J. M., & Kosters, W. A. (2005). Frequent itemsets for genomic profiling. In Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings (pp. 104-116). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3695 LNBI). Springer Verlag. https://doi.org/10.1007/11560500_10

De Graaf, Jeannette M. ; De Menezes, Renée X. ; Boer, Judith M. et al. / Frequent itemsets for genomic profiling. Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings. Springer Verlag, 2005. pp. 104-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Frequent itemsets for genomic profiling",

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.",

author = "\{De Graaf\}, \{Jeannette M.\} and \{De Menezes\}, \{Ren{\'e}e X.\} and Boer, \{Judith M.\} and Kosters, \{Walter A.\}",

year = "2005",

doi = "10.1007/11560500\_10",

language = "English",

isbn = "3540291040",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "104--116",

booktitle = "Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings",

address = "Germany",

note = "1st International Symposium on Computational Life Sciences, CompLife 2005 ; Conference date: 25-09-2005 Through 27-09-2005",

}

De Graaf, JM, De Menezes, RX, Boer, JM & Kosters, WA 2005, Frequent itemsets for genomic profiling. in Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3695 LNBI, Springer Verlag, pp. 104-116, 1st International Symposium on Computational Life Sciences, CompLife 2005, Konstanz, Germany, 25/09/05. https://doi.org/10.1007/11560500_10

Frequent itemsets for genomic profiling. / De Graaf, Jeannette M.; De Menezes, Renée X.; Boer, Judith M. et al.
Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings. Springer Verlag, 2005. p. 104-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3695 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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De Graaf JM, De Menezes RX, Boer JM, Kosters WA. Frequent itemsets for genomic profiling. In Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings. Springer Verlag. 2005. p. 104-116. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11560500_10

Frequent itemsets for genomic profiling

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