MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage
The challenges posed by climate change have had a crucial impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. Consequently, the identification of abiotic stress-responsive genes (SRGs) in crops is essential for augmenting their resilience. This study...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/11/1/44 |
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| author | Xiong You Yiting Shu Xingcheng Ni Hengmin Lv Jian Luo Jianping Tao Guanghui Bai Shusu Feng |
| author_facet | Xiong You Yiting Shu Xingcheng Ni Hengmin Lv Jian Luo Jianping Tao Guanghui Bai Shusu Feng |
| author_sort | Xiong You |
| collection | DOAJ |
| description | The challenges posed by climate change have had a crucial impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. Consequently, the identification of abiotic stress-responsive genes (SRGs) in crops is essential for augmenting their resilience. This study presents a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four abiotic stresses: cold, heat, drought, and salt. To construct this model, data from relevant studies regarding responses to these abiotic stresses were compiled, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. For the selected feature set, six distinct machine learning binary classification algorithms were employed. The results demonstrate that the constructed models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. For each type of stress, a distinct number of stress-resistant genes was predicted, and the ten genes with the highest scores were selected for further analysis. To facilitate the implementation of the proposed strategy by users, an online prediction server, has been developed. This study provides new insights into computational approaches to the identification of abiotic SRGs in Chinese cabbage as well as in other plants. |
| format | Article |
| id | doaj-art-6c8cd13e16a04e77ba62b9e0e2b767d5 |
| institution | Kabale University |
| issn | 2311-7524 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj-art-6c8cd13e16a04e77ba62b9e0e2b767d52025-01-24T13:34:35ZengMDPI AGHorticulturae2311-75242025-01-011114410.3390/horticulturae11010044MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese CabbageXiong You0Yiting Shu1Xingcheng Ni2Hengmin Lv3Jian Luo4Jianping Tao5Guanghui Bai6Shusu Feng7College of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaThe Institute of Agricultural Information, Jiangsu Province Academy of Agricultural Sciences, Nanjing 210014, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaThe challenges posed by climate change have had a crucial impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. Consequently, the identification of abiotic stress-responsive genes (SRGs) in crops is essential for augmenting their resilience. This study presents a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four abiotic stresses: cold, heat, drought, and salt. To construct this model, data from relevant studies regarding responses to these abiotic stresses were compiled, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. For the selected feature set, six distinct machine learning binary classification algorithms were employed. The results demonstrate that the constructed models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. For each type of stress, a distinct number of stress-resistant genes was predicted, and the ten genes with the highest scores were selected for further analysis. To facilitate the implementation of the proposed strategy by users, an online prediction server, has been developed. This study provides new insights into computational approaches to the identification of abiotic SRGs in Chinese cabbage as well as in other plants.https://www.mdpi.com/2311-7524/11/1/44Chinese cabbagestress-responsive genesmachine learningbinary classification |
| spellingShingle | Xiong You Yiting Shu Xingcheng Ni Hengmin Lv Jian Luo Jianping Tao Guanghui Bai Shusu Feng MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage Horticulturae Chinese cabbage stress-responsive genes machine learning binary classification |
| title | MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage |
| title_full | MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage |
| title_fullStr | MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage |
| title_full_unstemmed | MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage |
| title_short | MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage |
| title_sort | mlas machine learning based approach for predicting abiotic stress responsive genes in chinese cabbage |
| topic | Chinese cabbage stress-responsive genes machine learning binary classification |
| url | https://www.mdpi.com/2311-7524/11/1/44 |
| work_keys_str_mv | AT xiongyou mlasmachinelearningbasedapproachforpredictingabioticstressresponsivegenesinchinesecabbage AT yitingshu mlasmachinelearningbasedapproachforpredictingabioticstressresponsivegenesinchinesecabbage AT xingchengni mlasmachinelearningbasedapproachforpredictingabioticstressresponsivegenesinchinesecabbage AT hengminlv mlasmachinelearningbasedapproachforpredictingabioticstressresponsivegenesinchinesecabbage AT jianluo mlasmachinelearningbasedapproachforpredictingabioticstressresponsivegenesinchinesecabbage AT jianpingtao mlasmachinelearningbasedapproachforpredictingabioticstressresponsivegenesinchinesecabbage AT guanghuibai mlasmachinelearningbasedapproachforpredictingabioticstressresponsivegenesinchinesecabbage AT shusufeng mlasmachinelearningbasedapproachforpredictingabioticstressresponsivegenesinchinesecabbage |