Efficiency of Clustering Algorithms in Mining Biological databases Research Paper

Efficiency of Clustering Algorithms in Mining Biological databases - look into Paper ExampleFor example Hierarchical algorithms often work by either splitting or merging the groups being study in order to develop a hierarchy of clusters that is based on the sympatheticity of the sequences. On the other hand, partitioning algorithms work by partitioning the data sets being analyzed based on distance between them (Fayyad , 2003, 346). The selection of any type of meet algorithm should tho be primarily based on the nature of the sequences or clusters to be analyzed, the acceptable error expect as the availability of computational resources. This is particularly with regard to the fact that each of the categories of foregather algorithms has its own strengths and limitations and therefore suited for different tasks. Biological databases such as those involving the archeological site of protein or gene sequences are outflank analyzed using clustering algorithms because it provid es detailed exploratory epitome of the sequences. This paper critically analyzes the efficiency of clustering algorithms in the mining of biological databases such as gene sequences. Applications of clustering algorithms in analyzing gene sequences During the statistical analysis of biological databases, the choice of clustering, the choice of clustering algorithm often depends on the nature of data sets as well as the intended application of the results. In biological data mining closely of the sequences that are increasingly being analyzed using clustering algorithms include genomic as well as protein sequences. According to Werner (2008, 52), recent advances in bioinformatics have resulted in the increasing use of clustering algorithms in the analysis of both protein and gene sequences In the study of gene chemical formulas, clustering is one of the major exploratory techniques apply in the analysis of microarray slides containing hundreds of thousands of genes2. In such case s, clustering is employed to help group together similar genes and consequently enable the biologists to identify the relationship between the particular genes as well as crop the amount of information that is needed to be analyzed. Genes clustered together are usually co-regulated or share similar functions. Additionally when time series clustering methods are used, genes which exhibit similar characteristics at apt(p) times may be grouped together to indicate a possibility of co-regulation. Clustering algorithms commode also be efficiently used analyze gene samples on the basis of similar facial side patterns. Although expression patterns usually involve complex phenotypes, clustering analysis is one of the most effective techniques that push aside be used to identify arrays with similar or different phenotype characteristics. This application of clustering algorithms is particularly significant in medical researches where the approach allows medical scientists to identify different pathologies on the basis of the gene expression patterns as opposed to the common histological methods. In unsupervised cluster analysis of gene expression arrays, the major assumption is usually that genes of the same biological process should be clustered together whether in condition dependent or in time series. Another important area in the mining of genetic databases in which clustering algorithms is significantly used is the analysis of gene profiles. In this regard clustering algorithm is potentially important in the analysis of sub classes of diseases as well as in the detection of genes