Rice Genomes Classification based on Efficient Distance Measures Classifiers

The structure and composition of genomes is swiftly systematic in pace with their sequencing. The promising data show that a significant portion of Rice genomes is composed of transposable elements. Given the profusion and diversity of TEs and the hustle at which large quantities of sequence data are rising, detection and annotation of TEs presents a significant confront. Here we propose integrated classification system, designed on the basis of the transposition mechanism; sequence similarities and structural relationships can be easily applied by amateur. We used machine learning technique based on different classifier algorithms with four distance measures.