Document Detail

Latent factor analysis facilitates modelling of oncogenic genes for colon adenocarcinoma.
MedLine Citation:
PMID:  24067416     Owner:  NLM     Status:  In-Data-Review    
Identification of oncogenic genes from a large sample number of genomic data is a challenge. In this study, a well-established latent factor model, Bayesian factor and regression model, are applied to predict unknown colon cancer related genes from colon adenocarcinoma genomic data. Four important latent factors were addressed by the latent factor model, focusing on characterisation of heterogeneity of expression patterns of specific oncogenic genes by using microarray data of 174 colon cancer patients. Based on the fact that variables included in the same latent factor have some common characteristics and known cancer related genes in Online Mendelian Inheritance in Man, the authors found that the four latent factors can be employed to predict unknown colon cancer related genes that were never reported in the literature. The authors validated 15 identified genes by checking their somatic mutations of the same patients from DNA sequencing data.
Changhe Fu; Su Deng; Qiqing Song; Ling Jing
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IET systems biology     Volume:  7     ISSN:  1751-8849     ISO Abbreviation:  IET Syst Biol     Publication Date:  2013 Oct 
Date Detail:
Created Date:  2013-09-26     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101301198     Medline TA:  IET Syst Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  165-9     Citation Subset:  IM    
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