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Rai, Anil
- Classification of Cereal Proteins Related to Abiotic Stress Based on their Physicochemical Properties Using Support Vector Machine
Abstract Views :242 |
PDF Views:84
Authors
Affiliations
1 Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110 012, IN
1 Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110 012, IN
Source
Current Science, Vol 107, No 8 (2014), Pagination: 1283-1289Abstract
Abiotic stress factors severely limit plant growth and development as well as crop yield. There is a great need to develop understanding of plant physiological responses to abiotic stresses in order to improve crop productivity through crop improvement programmes. Proteins play a central role in plant adaptations under stress and hence their identification is important to the biologist. Identification of such proteins by wet lab experimentation is sometimes expensive and timeconsuming. In such a situation, in silico approaches can be used to narrow down this search. In this study, classification of cereal proteins subjected to four different stresses, namely, extreme temperature, drought, salt and abscisic acid (ABA) was undertaken. Classification models were built using support vector machine (SVM) to predict the function of proteins under these abiotic stresses on the basis of 34 physicochemical features extracted from the protein sequence. Specific features of the protein sequence that are highly correlated with certain protein functions were selected by stepwise logistic regression, a feature selection method. SVM was trained using different kernel functions and cross-validated using 10-fold crossvalidation technique. Prediction precision was assessed through different measures such as sensitivity, specificity and accuracy. The accuracy of protein function prediction using SVM with different kernel functions ranges from 60% to 100%.Keywords
Abiotic Stress, Cross-validation, Physicochemical Properties, Proteins, Support Vector Machine.Full Text
- Spatial Approach for the Estimation of Average Yield of Cotton Using Reduced Number of Crop Cutting Experiments
Abstract Views :45 |
PDF Views:39
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 Indian Council of Agricultural Research, New Delhi 110 001, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 Indian Council of Agricultural Research, New Delhi 110 001, IN
Source
Current Science, Vol 125, No 5 (2023), Pagination: 518-529Abstract
In India, cotton yield estimates are done using crop cutting experiments (CCEs) conducted within the framework of the general crop estimation surveys (GCES) methodology. In recent times, for obtaining reliable estimates at levels lower than the district, the number of CCEs has increased in comparison to the existing set-up of GCES. This puts an additional financial burden on Government agencies. There is a possibility of reducing the number of CCEs under the GCES methodology and predicting the remaining CCE points using an appropriate spatial prediction model. In this article, the predictive performance of different spatial models has been compared. Furthermore, district-level estimate of average productivity of cotton has been determined using the geographically weighted regression (GWR) technique and the results compared with those obtained using the traditional GCES methodology. The proposed spatial estimator of the average yield of cotton obtained using the GWR approach is more efficient and the results are comparable with the estimates obtained using the GCES methodology. The developed methodology can be utilized to reduce the number of CCEs and capture the spatial non-stationarity present in the cotton crop yield.Keywords
Cotton Yield, Crop Cutting Experiments, District Level, Geographically Weighted Regression, Spatial Non-Stationarity.Full Text
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