Application of data mining techniques on diabetes related proteins

Genomic Data is growing very rapidly with the sequencing of genomes of various forms of life. To understand the overwhelming data and to obtain meaningful information, Data Mining techniques such as Principal Component Analysis and Discriminant Analysis are used for the purpose. Data Mining is basically used when the data is vast and there is need to extract the hidden knowledge in the form of useful patterns. The data set taken into consideration is protein data pertaining to diabetes mellitus obtained from a database. The task at hand was to find out in which species most of the diabetes related proteins exist. It so happened that most of these proteins were prevalent in Human Beings, House Mice and Norway Rat as they are all mammals and Human Beings have orthologs as House Mice and Norway Rat. Both these techniques prove that human beings show a variation from those of House Mice and Norway Rat which are similar in terms of the variation of protein attributes. This can also be inferred from statistical analysis by using histograms and bivariate plots. Other Data Mining Techniques such as Regression and Clustering can be used to further explore the above inference.