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Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and...

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Autores principales: Shoaib, Muhammad, Shah, Babar, Sayed, Nasir, Ali, Farman, Ullah, Rafi, Hussain, Irfan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10612337/
https://www.ncbi.nlm.nih.gov/pubmed/37900739
http://dx.doi.org/10.3389/fpls.2023.1283235
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author Shoaib, Muhammad
Shah, Babar
Sayed, Nasir
Ali, Farman
Ullah, Rafi
Hussain, Irfan
author_facet Shoaib, Muhammad
Shah, Babar
Sayed, Nasir
Ali, Farman
Ullah, Rafi
Hussain, Irfan
author_sort Shoaib, Muhammad
collection PubMed
description Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.
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spelling pubmed-106123372023-10-29 Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection Shoaib, Muhammad Shah, Babar Sayed, Nasir Ali, Farman Ullah, Rafi Hussain, Irfan Front Plant Sci Plant Science Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications. Frontiers Media S.A. 2023-10-13 /pmc/articles/PMC10612337/ /pubmed/37900739 http://dx.doi.org/10.3389/fpls.2023.1283235 Text en Copyright © 2023 Shoaib, Shah, Sayed, Ali, Ullah and Hussain https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Shoaib, Muhammad
Shah, Babar
Sayed, Nasir
Ali, Farman
Ullah, Rafi
Hussain, Irfan
Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection
title Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection
title_full Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection
title_fullStr Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection
title_full_unstemmed Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection
title_short Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection
title_sort deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10612337/
https://www.ncbi.nlm.nih.gov/pubmed/37900739
http://dx.doi.org/10.3389/fpls.2023.1283235
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