DocumentCode :
1537415
Title :
Artificial neural network identification of heterotrophic marine bacteria based on their fatty-acid composition
Author :
Giacomini, Mauro ; Ruggiero, Carmelina ; Bertone, Stefania ; Calegari, Letizia
Author_Institution :
Dept. of Commun. Comput. & Syst. Sci., Genova Univ., Italy
Volume :
44
Issue :
12
fYear :
1997
Firstpage :
1185
Lastpage :
1191
Abstract :
The traditional approach to biochemical identification of marine fresh isolates requires considerably long culture preparation times and large quantities of expensive materials and reagents, and the results are not very reliable. On the other hand, taxonomy tests based on DNA composition, although sensitive and reliable, require long execution times and high costs, A method is presented for the classification of fatty-acid profiles, extracted from marine bacteria strains, at genus level based on supervised artificial neural networks. The proposed method allows the correct identification of all patterns belonging to the training set and almost all patterns belonging to the test set. Moreover, a quantitative measure of the importance of each fatty acid for bacterial classification is also achieved. This measure allows the determination of a cluster of fatty acids to be controlled with greater care. The results show that the proposed method is reproducible and rapid, so that it can be routinely used in the marine microbiology laboratory to identify fresh isolates.
Keywords :
biological techniques; biology computing; cellular biophysics; chemical analysis; laboratory techniques; organic compounds; DNA composition; artificial neural network identification; bacterial classification; biochemical identification; culture preparation time; fatty-acid composition; fresh isolates identification; genus level; heterotrophic marine bacteria; marine bacteria strains; marine microbiology laboratory; supervised artificial neural networks; taxonomy tests; training set; Artificial neural networks; Biological materials; Capacitive sensors; Costs; DNA; Laboratories; Materials reliability; Microorganisms; Taxonomy; Testing; Algorithms; Bacteria; Bacteriological Techniques; Fatty Acids; Neural Networks (Computer); Oceans and Seas; Sensitivity and Specificity; Water Microbiology;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/10.649990
Filename :
649990
Link To Document :
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