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Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 100 - Number 6 |
| Year of Publication: 2014 |
| Authors: Rajbir Singh, Neha Garg, Dheeraj Pal Kaur |
10.5120/17530-8103
|
Rajbir Singh, Neha Garg, Dheeraj Pal Kaur . Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data. International Journal of Computer Applications. 100, 6 ( August 2014), 31-36. DOI=10.5120/17530-8103
Abstract
Bioinformatics is a data intensive field of research and development. DNA microarray used to better understand form of saccharomyces cerevisiae disease such as cancer. Microarray allows us to diagnose and treat patients more successfully. Statistical method devoted to detection in DNA from microarray data, the inherent challenges in data quality associated with most filter techniques remains a challenging problem in microarray association studies. Applying methods of simulation studies and a genome-wide association microarray study in saccharomyces cerevisiae, that find current approach significantly improve DNA microarray cell reduces the yeast value rates and false positive genes variation. Clustering is the one of the main techniques for data mining. Microarray is the evolutionary history for a set of evolutionary related genes expression data. There are number of different distance based methods of which two are dealt with here: Euclidean method and Manhattan method. . A method for construction of distance based gens expression using clustering is proposed and implemented on different saccharomyces cerevisiae samples. Evolutionary distances between two or more genes are calculated using p-distance method. Multiple samples are applied on different datasets. Hierarchical clustering and k-mean clustering are constructed for different datasets from available data using both the distance based methods. Then, final cluster is constructed using these closely related filter dataset.
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