Automatic classification of fungal-fungal interactions using deep leaning models
Fungi provide valuable solutions for diverse biotechnological applications, such as enzymes in the food industry, bioactive metabolites for healthcare, and biocontrol organisms in agriculture. Current workflows for identifying new biocontrol fungi often rely on subjective visual observations of stra...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024004008 |
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| author | Marjan Mansourvar Jonathan Funk Søren Dalsgård Petersen Sajad Tavakoli Jakob Blæsbjerg Hoof David Llorente Corcoles Sabrina M. Pittroff Lars Jelsbak Niels Bjerg Jensen Ling Ding Rasmus John Normand Frandsen |
| author_facet | Marjan Mansourvar Jonathan Funk Søren Dalsgård Petersen Sajad Tavakoli Jakob Blæsbjerg Hoof David Llorente Corcoles Sabrina M. Pittroff Lars Jelsbak Niels Bjerg Jensen Ling Ding Rasmus John Normand Frandsen |
| author_sort | Marjan Mansourvar |
| collection | DOAJ |
| description | Fungi provide valuable solutions for diverse biotechnological applications, such as enzymes in the food industry, bioactive metabolites for healthcare, and biocontrol organisms in agriculture. Current workflows for identifying new biocontrol fungi often rely on subjective visual observations of strains’ performance in microbe-microbe interaction studies, making the process time-consuming and difficult to reproduce. To overcome these challenges, we developed an AI-automated image classification approach using machine learning algorithm based on deep neural network. Our method focuses on analyzing standardized images of 96-well microtiter plates with solid medium for fungal-fungal challenge experiments. We used our model to categorize the outcome of interactions between the plant pathogen Fusarium graminearum and individual isolates from a collection of 38,400 fungal strains. The authors trained multiple deep learning architectures and evaluated their performance. The results strongly support our approach, achieving a peak accuracy of 95.0 % with the DenseNet121 model and a maximum macro-averaged F1-Score of 93.1 across five folds. To the best of our knowledge, this paper introduces the first automated method for classifying fungal–fungal interactions using deep learning, which can easily be adapted for other fungal species. |
| format | Article |
| id | doaj-art-ac965e91f07b4b6f899f54a9468bc2b4 |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-ac965e91f07b4b6f899f54a9468bc2b42024-11-25T04:40:59ZengElsevierComputational and Structural Biotechnology Journal2001-03702024-12-012342224231Automatic classification of fungal-fungal interactions using deep leaning modelsMarjan Mansourvar0Jonathan Funk1Søren Dalsgård Petersen2Sajad Tavakoli3Jakob Blæsbjerg Hoof4David Llorente Corcoles5Sabrina M. Pittroff6Lars Jelsbak7Niels Bjerg Jensen8Ling Ding9Rasmus John Normand Frandsen10Corresponding author.; Department of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkDepartment of Biotechnology and Biomedicine (DTU Bioengineering) Technical University of Denmark, Søltofts Plads, Kgs. Lyngby 2800, DenmarkFungi provide valuable solutions for diverse biotechnological applications, such as enzymes in the food industry, bioactive metabolites for healthcare, and biocontrol organisms in agriculture. Current workflows for identifying new biocontrol fungi often rely on subjective visual observations of strains’ performance in microbe-microbe interaction studies, making the process time-consuming and difficult to reproduce. To overcome these challenges, we developed an AI-automated image classification approach using machine learning algorithm based on deep neural network. Our method focuses on analyzing standardized images of 96-well microtiter plates with solid medium for fungal-fungal challenge experiments. We used our model to categorize the outcome of interactions between the plant pathogen Fusarium graminearum and individual isolates from a collection of 38,400 fungal strains. The authors trained multiple deep learning architectures and evaluated their performance. The results strongly support our approach, achieving a peak accuracy of 95.0 % with the DenseNet121 model and a maximum macro-averaged F1-Score of 93.1 across five folds. To the best of our knowledge, this paper introduces the first automated method for classifying fungal–fungal interactions using deep learning, which can easily be adapted for other fungal species.http://www.sciencedirect.com/science/article/pii/S2001037024004008Fungal growthDeep learningMachine learningComputer visionAutomationBiocontrol |
| spellingShingle | Marjan Mansourvar Jonathan Funk Søren Dalsgård Petersen Sajad Tavakoli Jakob Blæsbjerg Hoof David Llorente Corcoles Sabrina M. Pittroff Lars Jelsbak Niels Bjerg Jensen Ling Ding Rasmus John Normand Frandsen Automatic classification of fungal-fungal interactions using deep leaning models Computational and Structural Biotechnology Journal Fungal growth Deep learning Machine learning Computer vision Automation Biocontrol |
| title | Automatic classification of fungal-fungal interactions using deep leaning models |
| title_full | Automatic classification of fungal-fungal interactions using deep leaning models |
| title_fullStr | Automatic classification of fungal-fungal interactions using deep leaning models |
| title_full_unstemmed | Automatic classification of fungal-fungal interactions using deep leaning models |
| title_short | Automatic classification of fungal-fungal interactions using deep leaning models |
| title_sort | automatic classification of fungal fungal interactions using deep leaning models |
| topic | Fungal growth Deep learning Machine learning Computer vision Automation Biocontrol |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024004008 |
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