DocumentCode :
2453451
Title :
Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques
Author :
Dittman, David J. ; Khoshgoftaar, Taghi M. ; Wald, Randall ; Van Hulse, Jason
Author_Institution :
Florida Atlantic Univ., Boca Raton, FL, USA
fYear :
2010
fDate :
12-14 Dec. 2010
Firstpage :
147
Lastpage :
152
Abstract :
One of today´s most important scientific research topics is discovering the genetic links between cancers. This paper contains the results of a comparison of three different cancers (breast, colon, and lung) based on the results of feature selection techniques on a data set created from DNA micro array data consisting of samples from all three cancers. The data was run through a set of eighteen feature rankers which ordered the genes by importance with respect to a targeted cancer. This process was repeated three times, each time with a different target cancer. The rankings were then compared, keeping each feature ranker static while varying the cancers being compared. The cancers were evaluated both in pairs and all together, for matching genes. The results of the comparison show a large correlation between the two known hereditary cancers, breast and colon, and little correlation between lung cancer and the other cancers. This is the first study to apply eighteen different feature rankers in a bioinformatics case study, eleven of which were recently proposed and implemented by our research team.
Keywords :
DNA; bioinformatics; cancer; data mining; lung; DNA microarray data; bioinformatics; breast cancer; colon cancer; feature selection technique; lung cancer; Breast; Cancer; Colon; Lungs; Measurement; Probes; Radio frequency; Breast Cancer; Colon Cancer; DNA Microarray Data; Feature Selection; Lung Cancer;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
Type :
conf
DOI :
10.1109/ICMLA.2010.29
Filename :
5708826
Link To Document :
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