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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9316258 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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