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Applied and Environmental Microbiology, September 2005, p. 5244-5253, Vol. 71, No. 9
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.9.5244-5253.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Artificial Neural Network Prediction of Viruses in Shellfish

Gail Brion,1* Chandramouli Viswanathan,2 T. R. Neelakantan,3 Srinivasa Lingireddy,1 Rosina Girones,4 David Lees,5 Annika Allard,6 and Apostolos Vantarakis7

Department of Civil Engineering, University of Kentucky, Lexington, Kentucky 40506,1 Indian Institute of Technology, Guwahati, Assam, India,2 School of Civil Engineering, SASTRA Deemed University, Thanjavur 613402, India,3 Biology School, University of Barcelona, Barcelona, Spain,4 Centre for Environment, Fisheries and Aquaculture Science, Weymouth, United Kingdom,5 Umeå University Hospital, Umeå, Sweden,6 School of Medicine, University of Patras, Patras, Greece7

Received 7 August 2004/ Accepted 30 March 2005

A database was probed with artificial neural network (ANN) and multivariate logistic regression (MLR) models to investigate the efficacy of predicting PCR-identified human adenovirus (ADV), Norwalk-like virus (NLV), and enterovirus (EV) presence or absence in shellfish harvested from diverse countries in Europe (Spain, Sweden, Greece, and the United Kingdom). The relative importance of numerical and heuristic input variables to the ANN model for each country and for the combined data was analyzed with a newly defined relative strength effect, which illuminated the importance of bacteriophages as potential viral indicators. The results of this analysis showed that ANN models predicted all types of viral presence and absence in shellfish with better precision than MLR models for a multicountry database. For overall presence/absence classification accuracy, ANN modeling had a performance rate of 95.9%, 98.9%, and 95.7% versus 60.5%, 75.0%, and 64.6% for the MLR for ADV, NLV, and EV, respectively. The selectivity (prediction of viral negatives) was greater than the sensitivity (prediction of viral positives) for both models and with all virus types, with the ANN model performing with greater sensitivity than the MLR. ANN models were able to illuminate site-specific relationships between microbial indicators chosen as model inputs and human virus presence. A validation study on ADV demonstrated that the MLR and ANN models differed in sensitivity and selectivity, with the ANN model correctly identifying ADV presence with greater precision.

* Corresponding author. Mailing address: Dept. of Civil Engineering, University of Kentucky, 161 Raymond Bldg., Lexington, KY 40506-0281. Phone: (859) 257-4467. Fax: (859) 257-4404. E-mail: gbrion{at}engr.uky.edu.

Applied and Environmental Microbiology, September 2005, p. 5244-5253, Vol. 71, No. 9
0099-2240/05/$08.00+0     doi:10.1128/AEM.71.9.5244-5253.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.





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