Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4914
Title: Mining frequent patterns from microarray data
Authors: Yıldız, Barış
Şelale, Hatice
Şelale, Hatice
Keywords: Data mining
Microarray
Frequent pattern mining
Bioinformatics
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Yıldız, B., and Şelale, H. (2011, May 2-5). Mining frequent patterns from microarray data. Paper presented at the 6th International Symposium on Health Informatics and Bioinformatics. doi:10.1109/HIBIT.2011.6450819
Abstract: The rapid development of computers and increasing amount of collected data made data mining a popular analysis tool. Data mining research is interrelated to many fields and one of the most important ones is bioinformatics. Among many techniques, mining association rules or frequent patterns is one of the most studied techniques in computer science and it is applicable to bioinformatics. Association analysis of gene expressions may be used as decision support mechanism for finding genes that are in same pathway. In this work, publicly available yeast microarray data has been analyzed using an efficient frequent pattern mining algorithm Matrix Apriori and frequently co-over-expressed genes have been identified. © 2011 IEEE.
Description: 6th International Symposium on Health Informatics and Bioinformatics, HIBIT 2011; Izmir; Turkey; 2 May 2011 through 5 May 2011
URI: http://doi.org/10.1109/HIBIT.2011.6450819
http://hdl.handle.net/11147/4914
ISBN: 9781450775342
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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