Brief Communication: Classification and identification of mosquito species using artificial neural networks

Authors:
Amit Kumar Banerjee;K. Kiran;U. S. N. Murty;Ch. Venkateswarlu
Affiliations:
Bioinformatics Group, Biology Division, Indian Institute of Chemical Technology, Hyderabad 500007, Andhra Pradesh, India;Chemical Engineering Sciences Division, Indian Institute of Chemical Technology, Hyderabad 500007, Andhra Pradesh, India;Bioinformatics Group, Biology Division, Indian Institute of Chemical Technology, Hyderabad 500007, Andhra Pradesh, India;Chemical Engineering Sciences Division, Indian Institute of Chemical Technology, Hyderabad 500007, Andhra Pradesh, India
Venue:
Computational Biology and Chemistry
Year:
2008

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Abstract

An artificial neural network method is presented for classification and identification of Anopheles mosquito species based on the internal transcribed spacer2 (ITS2) data of ribosomal DNA string. The method is implemented in two different multi-layered feed-forward neural network model forms, namely, multi-input single-output neural network (MISONN) and multi-input multi-output neural network (MIMONN). A number of data sequences in varying sizes of different Anopheline malarial vectors and their corresponding species coding are employed to develop the neural network models. The classification efficiency of the network models for untrained data sequences is evaluated in terms of quantitative performance criteria. The results demonstrate the efficiency of the neural network models to extract the genetic information in ITS2 sequences and to adapt to new data. The method of MISONN is found to exhibit superior performance over MIMONN in distinguishing and identification of the mosquito vectors.