Protein sequence for clustering DNA based on Artificial Neural Networks

DNA is a nucleic acid that contains the genetic instructions used in the development and functioning of all known living organisms and some viruses. Clustering is a process that groups a set of objects into clusters so that the similarity among objects in the same cluster is high, while that among the objects in different clusters is low. In this paper, we proposed an approach for clustering DNA sequences using Self-Organizing Map (SOM) algorithm and Protein Sequence. The main objective is to analyze biological data and to bunch DNA to many clusters more easily and efficiently. We use the proposed approach to analyze both large and small amount of input DNA sequences. The results show that the similarity of the sequences does not depend on the amount of input sequences. Our approach depends on evaluating the degree of the DNA sequences similarity using the hierarchal representation Dendrogram. Representing large amount of data using hierarchal tree gives the ability to compare large sequences efficiently