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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › peer review

2 Citaten (Scopus)

Samenvatting

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-2	Engels
Titel	Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings
Uitgeverij	Springer Verlag
Pagina's	104-116
Aantal pagina's	13
ISBN van geprinte versie	3540291040, 9783540291046
DOI's	https://doi.org/10.1007/11560500_10
Status	Gepubliceerd - 2005
Extern gepubliceerd	Ja
Evenement	1st International Symposium on Computational Life Sciences, CompLife 2005 - Konstanz, Duitsland Duur: 25 sep. 2005 → 27 sep. 2005

Publicatie series

Naam	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3695 LNBI
ISSN van geprinte versie	0302-9743
ISSN van elektronische versie	1611-3349

Congres

Congres	1st International Symposium on Computational Life Sciences, CompLife 2005
Land/Regio	Duitsland
Stad	Konstanz
Periode	25/09/05 → 27/09/05

Toegang tot document

10.1007/11560500_10

Citeer dit

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 (blz. 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. blz. 104-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e8666c7f1fdb4dd48f49a689716cb37e,

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, blz. 104-116, 1st International Symposium on Computational Life Sciences, CompLife 2005, Konstanz, Duitsland, 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. blz. 104-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3695 LNBI).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › peer review

TY - GEN

T1 - Frequent itemsets for genomic profiling

AU - De Graaf, Jeannette M.

AU - De Menezes, Renée X.

AU - Boer, Judith M.

AU - Kosters, Walter A.

PY - 2005

Y1 - 2005

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.

UR - http://www.scopus.com/inward/record.url?scp=33646201321&partnerID=8YFLogxK

U2 - 10.1007/11560500_10

DO - 10.1007/11560500_10

M3 - Conference contribution

AN - SCOPUS:33646201321

SN - 3540291040

SN - 9783540291046

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 104

EP - 116

BT - Computational Life Sciences - First International Symposium, CompLife 2005, Proceedings

PB - Springer Verlag

T2 - 1st International Symposium on Computational Life Sciences, CompLife 2005

Y2 - 25 September 2005 through 27 September 2005

ER -

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. blz. 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

Samenvatting

Publicatie series

Congres

Toegang tot document

Vingerafdruk

Citeer dit