DNA data clustering by combination of 3D cellular automata and n-grams for structure molecule prediction. (2016) - Kabli, Fatima; Hamou, Reda Mohamed; Amine, Abdelmalek
Knowledge extraction from genomic data is important activity for the biologist. In order to mine the underlying biological knowledge, we based on the Knowledge Discovery in Databases (KDD) process. In this paper, we transformed DNA sequences into texts: the text indexed by TF-IDF and n-grams approach. In the aim of grouping the similar DNA sequences, we applied the bio-inspired 3D cellular automata for clustering method. For the analysis of clustering results we based on the transformation of each DNA sequence into amino acids sequence; according to the standard genetic code, we concluded that the clusters help the biologists to select DNA sequences that can produce a type of medicament (molecule) and their various derivatives (low concentration in their composition).