ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants
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Title |
ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants
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Creator |
Prabina Kumar Meher
Tanmaya Kumar Sahu Ajit Gupta Anuj Kumar Sachin Rustgi |
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Subject |
ACC,autocross covariance ; ADB ,adaptive boosting ; auPRC, area under the precision-recall curve; auROC, area under the receiver operatingcharacteristic curve; GWAS, genome-wide association study; ML, machine learning; RFE, recursive feature elimination; SRG, stress-responsive gene; SVM,support vector machine; XGB, extreme gradient boosting.
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Description |
Not Available
One of the thrust areas of research in plant breeding is to develop crop cultivars with enhanced tolerance to abiotic stresses. Thus, identifying abiotic stress-responsive genes (SRGs) and proteins is important for plant breeding research. However, identifying such genes via established genetic approaches is laborious and resource intensive. Although transcriptome profiling has remained a reliable method of SRG identification, it is species specific. Additionally, identifying multistress responsive genes using gene expression studies is cumbersome. Thus, endorsing the need to develop a computational method for identifying the genes associated with different abiotic stresses. In this work, we aimed to develop a computational model for identifying genes responsive to six abiotic stresses: cold, drought, heat, light, oxidative, and salt. The predictions were performed using support vector machine (SVM), random forest, adaptive boosting (ADB), and extreme gradient boosting (XGB), where the autocross covariance (ACC) and K-mer compositional features were used as input. With ACC, K-mer, and ACC + K-mer compositional features, the overall accuracy of ∼60–77, ∼75–86, and ∼61–78% were respectively obtained using the SVM algorithm with fivefold cross-validation. The SVM also achieved higher accuracy than the other three algorithms. The proposed model was also assessed with an independent dataset and obtained an accuracy consistent with cross-validation. The proposed model is the first of its kind and is expected to serve the requirement of experimental biologists; however, the prediction accuracy was modest. Given its importance for the research community, the online prediction application, ASRpro, is made freely available (https://iasri-sg.icar.gov.in/asrpro/) for predicting abiotic SRGs and proteins. Not Available |
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Date |
2023-05-08T10:02:31Z
2023-05-08T10:02:31Z 2022-09-13 |
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Type |
Research Paper
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Identifier |
Not Available
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/76968 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Not Available
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