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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
 
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.
 
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.
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Date 2023-05-08T10:02:31Z
2023-05-08T10:02:31Z
2022-09-13
 
Type Research Paper
 
Identifier Not Available
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http://krishi.icar.gov.in/jspui/handle/123456789/76968
 
Language English
 
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Publisher Not Available