A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Learning an un-Supervised – Clustering Algorithm Monte Carlo over Consensus Clustering for Genomic Data for Tumor Identification
2019
International journal of recent technology and engineering
Clustering involves the grouping of similar objects into a set known as cluster. Objects in one cluster are likely to be different when compared to objects grouped under another cluster. Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. Subgroup classification is a basic task in high-throughput genomic data analysis, especially for gene expression and methylation data analysis. Mostly, unsupervised clustering methods are
doi:10.35940/ijrte.d7370.118419
fatcat:unvmsfxyibcdvadywkwhrszljq