Selecting informative and discriminative genes from huge microarray gene expression data is an important and challenging bioinformatics research topic.
Selecting informative and discriminative genes from huge microarray gene expression data is an important and challenging bioinformatics research topic. This.
A fuzzy-granular method for the gene selection task, where genes are grouped into different function granules with the fuzzy C-means algorithm (FCM) and ...
This paper proposes a fuzzy-granular method for the gene selection task. Firstly, genes are grouped into different function granules with the Fuzzy C-Means ...
Fuzzy-Granular Gene Selection from Microarray Expression Data
www.researchgate.net › ... › Microarray
Selecting informative and discriminative genes from huge microarray gene expression data is an important and challenging bioinformatics research topic.
This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy ...
Oct 1, 2017 · Thus, the gene shaving method is usually used for feature selection problem of the gene expression data which will be covered in more detail ...
People also ask
What is microarray gene expression data?
What is clustering of gene expression microarray data used for?
How do you analyze gene expression data?
How does rna seq data compare with gene expression microarrays?
Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since ...
Missing: Granular | Show results with:Granular
Fuzzy if-then rule base classifier has been employed in the research for classifying the microarray gene expression data through important gene selection.
[PDF] Recursive Fuzzy Granulation for Gene Subsets Extraction and ...
www.semanticscholar.org › paper
A type 2 fuzzy logic approach is used in microarray gene expression data to convert the numerical values into fuzzy terms, and after fuzzification, the ...