Neural network integration of gas-chromatographic and electrophoretic data for the identification of environmental bacteria

Author

Bertone, S. ; Giacomini, M. ; Soumetz, F. Caneva ; Ruggiero, C. ; Calegari, L.

Author_Institution

RILAB s.r.l., Genoa, Italy

Volume

2

fYear

1999

fDate

36434

Abstract

An identification method for environmental bacteria is presented, based on neural network elaboration of fatty acid gas-chromatographic data and protein electrophoretic data. The reliability of identification for 99 bacterial strains was 91%, showing that the method is better than a traditional statistical approach

Keywords

biochemistry; biological techniques; biology computing; chromatography; electrophoresis; microorganisms; proteins; self-organising feature maps; unsupervised learning; electrophoretic data; environmental bacteria identification; fatty acid gas-chromatographic data; gas-chromatographic data; marine bacterial strains; neural network integration; protein electrophoretic data; reliability; Artificial neural networks; Biochemical analysis; Biochemistry; Capacitive sensors; Medical diagnostic imaging; Microorganisms; Neural networks; Protein engineering; Stacking; Systematics;

fLanguage

English

Publisher

ieee

Conference_Titel

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint

Conference_Location

Atlanta, GA

ISSN

1094-687X

Print_ISBN

0-7803-5674-8

Type

conf

DOI

10.1109/IEMBS.1999.804087

Filename

804087