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Species classification from DNA sequences remains as an open challenge in the area of bioinformatics, which deals with the collection, processing and analysis of DNA and proteomic sequence. Though incorporation of data mining can guide the process to perform well, poor definition, and heterogeneous nature of gene sequence remains as a barrier. In this paper, an effective classification technique to identify the organism from its gene sequence is proposed. The proposed integrated technique is mainly based on pattern mining and neural network-based classification. In pattern mining, the technique mines nucleotide patterns and their support from selected DNA sequence. The high dimension of the mined dataset is reduced using Multilinear Principal Component Analysis (MPCA). In classification, a well- trained neural network classifies the selected gene sequence and so the organism is identified even from a part of the sequence. The proposed technique is evaluated by performing 10-fold cross validation, a statistical validation measure, and the obtained results prove the efficacy of the technique.

An Effective Identification of Species from DNA Sequence: A Classification Technique by Integrating DM and ANN