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LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction

Plant leaves, which convert light energy into chemical energy, serve as a major food source on Earth. The decrease in crop yield and quality is caused by plant leaf premature senescence. It is important to detect senescence-associated genes. In this study, we collected 5853 genes from a leaf senesce...

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Detalles Bibliográficos
Autores principales: Li, Zhidong, Tang, Wei, You, Xiong, Hou, Xilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316258/
https://www.ncbi.nlm.nih.gov/pubmed/35888183
http://dx.doi.org/10.3390/life12071095
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author Li, Zhidong
Tang, Wei
You, Xiong
Hou, Xilin
author_facet Li, Zhidong
Tang, Wei
You, Xiong
Hou, Xilin
author_sort Li, Zhidong
collection PubMed
description Plant leaves, which convert light energy into chemical energy, serve as a major food source on Earth. The decrease in crop yield and quality is caused by plant leaf premature senescence. It is important to detect senescence-associated genes. In this study, we collected 5853 genes from a leaf senescence database and developed a leaf-senescence-associated genes (SAGs) prediction model using the support vector machine (SVM) and XGBoost algorithms. This is the first computational approach for predicting SAGs with the sequence dataset. The SVM-PCA-Kmer-PC-PseAAC model achieved the best performance (F1score = 0.866, accuracy = 0.862 and receiver operating characteristic = 0.922), and based on this model, we developed a SAGs prediction tool called “SAGs_Anno”. We identified a total of 1,398,277 SAGs from 3,165,746 gene sequences from 83 species, including 12 lower plants and 71 higher plants. Interestingly, leafy species showed a higher percentage of SAGs, while leafless species showed a lower percentage of SAGs. Finally, we constructed the Leaf SAGs Annotation Platform using these available datasets and the SAGs_Anno tool, which helps users to easily predict, download, and search for plant leaf SAGs of all species. Our study will provide rich resources for plant leaf-senescence-associated genes research.
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spelling pubmed-93162582022-07-27 LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction Li, Zhidong Tang, Wei You, Xiong Hou, Xilin Life (Basel) Communication Plant leaves, which convert light energy into chemical energy, serve as a major food source on Earth. The decrease in crop yield and quality is caused by plant leaf premature senescence. It is important to detect senescence-associated genes. In this study, we collected 5853 genes from a leaf senescence database and developed a leaf-senescence-associated genes (SAGs) prediction model using the support vector machine (SVM) and XGBoost algorithms. This is the first computational approach for predicting SAGs with the sequence dataset. The SVM-PCA-Kmer-PC-PseAAC model achieved the best performance (F1score = 0.866, accuracy = 0.862 and receiver operating characteristic = 0.922), and based on this model, we developed a SAGs prediction tool called “SAGs_Anno”. We identified a total of 1,398,277 SAGs from 3,165,746 gene sequences from 83 species, including 12 lower plants and 71 higher plants. Interestingly, leafy species showed a higher percentage of SAGs, while leafless species showed a lower percentage of SAGs. Finally, we constructed the Leaf SAGs Annotation Platform using these available datasets and the SAGs_Anno tool, which helps users to easily predict, download, and search for plant leaf SAGs of all species. Our study will provide rich resources for plant leaf-senescence-associated genes research. MDPI 2022-07-21 /pmc/articles/PMC9316258/ /pubmed/35888183 http://dx.doi.org/10.3390/life12071095 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Li, Zhidong
Tang, Wei
You, Xiong
Hou, Xilin
LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction
title LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction
title_full LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction
title_fullStr LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction
title_full_unstemmed LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction
title_short LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction
title_sort lsap: a machine learning method for leaf-senescence-associated genes prediction
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316258/
https://www.ncbi.nlm.nih.gov/pubmed/35888183
http://dx.doi.org/10.3390/life12071095
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