scholarly journals Stimulatory Effects of Androgens on Eel Primary Ovarian Development - from Phenotypes to Genotypes

2021 ◽  
Author(s):  
Yung-Sen Huang ◽  
Chung-Yen Lin
Keyword(s):  
Gene Expression ◽  
Density Estimation ◽  
Ovarian Development ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Kernel Density ◽  
Genomic Data ◽  
Japanese Eel ◽  
Treatment Change ◽  
Ovarian Status

Androgens stimulate primary ovarian development in Vertebrate. Japanese eels underwent operation to sample the pre- and post-treated ovarian tissues from the same individual. Ovarian phenotypic or genotypic data were mined in a pair. A correlation between the initial ovarian status (determined by kernel density estimation (KDE), presented as a probability density of oocyte size) and the consequence of androgen (17MT) treatment (change in ovary) has been showed. The initial ovarian status appeared to be important to influence ovarian androgenic sensitivity. The initial ovary was important to the outcomes of androgen treatments, and ePAV (expression presence-absence variation) is existing in Japanese eel by analyze DEGs; core, unique, or accessory genes were identified, the sensitivities of initial ovaries were correlated with their gene expression profiles. We speculated the importance of genetic differential expression on the variations of phenotypes by 17MT, and transcriptomic approach seems to allow extracting multiple layers of genomic data.

Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods

2009 ◽  
pp. 221-238 ◽  
Author(s):  
George Sakellaropoulos ◽  
Antonis Daskalakis ◽  
George Nikiforidis ◽  
Christos Argyropoulos
Keyword(s):  
Gene Expression ◽  
Maximum Entropy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Kernel Density ◽  
Modeling Tools ◽  
Maximum Entropy Modeling ◽  
Model Free ◽  
Biological Entities ◽  
Density Methods

The presentation and interpretation of microarray-based genome-wide gene expression profiles as complex biological entities are considered to be problematic due to their featureless, dense nature. Furthermore microarray images are characterized by significant background noise, but the effects of the latter on the holistic interpretation of gene expression profiles remains under-explored. We hypothesize that a framework combining (a) Bayesian methodology for background adjustment in microarray images with (b) model-free modeling tools, may serve the dual purpose of data and model reduction, exposing hitherto hidden features of gene expression profiles. Within the proposed framework, microarray image restoration and noise adjustment is facilitated by a class of prior Maximum Entropy distributions. The resulting gene expression profiles are non-parametrically modeled by kernel density methods, which not only normalize the data, but facilitate the generation of reduced mathematical descriptions of biological variability as mixture models.

1327: Gene Expression Profiles in Benign Prostatic Hyperplasia

2004 ◽  
Vol 171 (4S) ◽  
pp. 349-350
Author(s):  
Gaelle Fromont ◽  
Michel Vidaud ◽  
Alain Latil ◽  
Guy Vallancien ◽  
Pierre Validire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Benign Prostatic Hyperplasia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostatic Hyperplasia
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