An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity.

Journal: Combinatorial chemistry & high throughput screening

Published Date: Jan 1, 2018

Abstract

AIM AND OBJECTIVE: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description.

Authors

Luis Francisco Barbosa-Santillán

University of Guadalajara, Zapopan, Jalisco, Mexico.
María de Los Angeles Calixto-Romo

The College of the South Frontier (ECOSURCONACyT), Tapachula, Chiapas, Mexico.
Juan Jaime Sánchez-Escobar

Technical and Industrial Teaching Center, Guadalajara, Jalisco, Mexico.
Liliana Ibeth Barbosa-Santillán

University of Guadalajara, Zapopan, Jalisco, Mexico.

Keywords

Cellulose Color Coloring Agents Congo Red Gene Library Hydrolysis Machine Learning Models, Molecular Neural Networks, Computer Pattern Recognition, Automated

External Resources

View on PubMed Access via DOI PubMed (30569862)

An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity.

Abstract

Authors

Keywords

External Resources

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