Identifying the most appropriate pattern for identification of gene expression changes in ovarian cancer using microarray
Joint Authors
Chaichian, Shahla
Moazzami, Bahram
Iskandari, Masumi
Faroughi, Pouya
Karimi, Asrin
Jasmi, Fatimah
DeKoning, Jenefer J.
Source
Iranian Red Crescent Medical Journal
Issue
Vol. 21, Issue 7 (31 Jul. 2019), pp.1-10, 10 p.
Publisher
Publication Date
2019-07-31
Country of Publication
United Arab Emirates
No. of Pages
10
Main Subjects
Topics
Abstract EN
Background: Microarray technology is an accurate method for recognition of disease association gene alterations.
However, there still is not an effective approach for the evaluation of gene expression in ovarian cancer.
Objectives: A reliable approach is described to identify genes associated with ovarian cancer.
Methods: Microarray gene expression data analysis was applied to correct systematic differences through four different normalization methods; LOESS, 3D LOESS, and neural network (NN3, NN4).
Then, three different clustering methods of K-means, fuzzy C-means, and hierarchical methods were examined on corrected gene expression values.
The proposed approach was tested on a reliable source of genes’ information, where the entropy of genes in samples and Euclidean distance were used for gene selection.
Results: Our findings revealed that a neural-network-based normalizationmethodcould better control the effects of non-biological variations from microarray data.
Moreover, the hierarchical clustering was more effective compared to other methods, and resulted in the identification of three genes, including BC029410, DUSP2, and ILDR1, as candidates for disease-association genes.
Conclusions: According to the finding of the present study, hierarchical clustering with nonlinear-based normalization could have the ability to prioritize genes for ovarian cancer.
American Psychological Association (APA)
Iskandari, Masumi& Chaichian, Shahla& DeKoning, Jenefer J.& Moazzami, Bahram& Faroughi, Pouya& Karimi, Asrin…[et al.]. 2019. Identifying the most appropriate pattern for identification of gene expression changes in ovarian cancer using microarray. Iranian Red Crescent Medical Journal،Vol. 21, no. 7, pp.1-10.
https://search.emarefa.net/detail/BIM-892343
Modern Language Association (MLA)
Iskandari, Masumi…[et al.]. Identifying the most appropriate pattern for identification of gene expression changes in ovarian cancer using microarray. Iranian Red Crescent Medical Journal Vol. 21, no. 7 (Jul. 2019), pp.1-10.
https://search.emarefa.net/detail/BIM-892343
American Medical Association (AMA)
Iskandari, Masumi& Chaichian, Shahla& DeKoning, Jenefer J.& Moazzami, Bahram& Faroughi, Pouya& Karimi, Asrin…[et al.]. Identifying the most appropriate pattern for identification of gene expression changes in ovarian cancer using microarray. Iranian Red Crescent Medical Journal. 2019. Vol. 21, no. 7, pp.1-10.
https://search.emarefa.net/detail/BIM-892343
Data Type
Journal Articles
Language
English
Notes
Text in English ; abstracts in .
Record ID
BIM-892343