S.J.Saritha¹ and P.Govindarajulu², ¹JNTUACE, India and ²S.V.University, India

Most current gene detection systems are Bio-informatics based methods. Despite the number of Bio-informatics based gene detection algorithms applied to CEGMA (Core Eukaryotic Genes Mapping Approach) dataset, none of them have introduced a pre-model to increase the accuracy and time reduction in the different CEGMA datasets. This method enables us to significantly reduce the time consumption for gene detection and increases the accuracy in the different datasets without loss of Information. This method is based on feature based Principal Component Analysis (FPCA). It works by projecting data elements onto a feature space, which is actually a vector space that spans the significant variations among known data elements.

Gene detection system, PCA, KPCA, Spectral simulation