Reference : Deep neural networks outperform human expert's capacity in characterizing bioleaching...
Scientific journals : Article
Life sciences : Biotechnology
Life sciences : Environmental sciences & ecology
Systems Biomedicine
http://hdl.handle.net/10993/39516
Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition
English
Buetti-Dinh, Antoine mailto [Università della Svizzera italiana - USI > Institute of Computational Science, Faculty of Informatics > > ; Swiss Institute of Bioinformatics - SIB]
Galli, Vanni [University of Applied Sciences of Southern Switzerland > Institute for Information Systems and Networking]
Bellenberg, Sören [Universität Duisburg-Essen > Fakultät für Chemie, Biofilm Centre]
Ilie, Olga [Università della Svizzera italiana - USI > Institute of Computational Science, Faculty of Informatics > > ; Swiss Institute of Bioinformatics - SIB]
Herold, Malte mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Christel, Stephan []
Boretska, Mariia []
Pivkin, Igor V. []
Wilmes, Paul mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Sand, Wolfgang []
Vera, Mario []
Dopson, Mark []
7-Mar-2019
Biotechnology Reports
Elsevier
Yes
International
2215-017X
Netherlands
[en] Acidophiles ; Bacterial biofilm ; Biomining ; Deep learning ; Convolutional neural networks ; Microscopy imaging
[en] Background

Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases.
Methods

The ability of deep neural networks versus human experts to classify microscopy images was tested on biofilm colonization patterns formed on sulfide minerals composed of up to three different bioleaching bacterial species attached to chalcopyrite sample particles.
Results

A low number of microscopy images per category (<600) was sufficient for highly efficient computational analysis of the biofilm's bacterial composition. The use of deep neural networks reached an accuracy of classification of ∼90% compared to ∼50% for human experts.
Conclusions

Deep neural networks outperform human experts’ capacity in characterizing bacterial biofilm composition involved in the degradation of chalcopyrite. This approach provides an alternative to standard, time-consuming biochemical methods.
Fonds National de la Recherche - FnR
Researchers
http://hdl.handle.net/10993/39516
10.1016/j.btre.2019.e00321
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430008/
FP7 ; 321567 - ERASYSAPP - ERASysAPP - Systems Biology Applications

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