Subhrajit Satpathy

TL;DR: This work has proposed a computational method for predicting subcellular localizations of miRNAs based on principal component scores of thermodynamic, structural properties and pseudo compositions of di-nucleotides, which achieved higher accuracy than the existing methods.

TL;DR: Wang et al. as discussed by the authors used Support Vector Machine (SVM) with seven kernels, i.e., linear, polynomial, radial, sigmoid, hyperbolic, Bessel and Laplace, to identify the proteins encoded by the circadian genes.

TL;DR: This is the first attempt as far as comprehensive evaluation of sequence encoding schemes for prediction of splice sites is concerned and prediction accuracy across species, the SVM-FDTF combination was optimum than other combinations of classifiers and encoding schemes.

TL;DR: In this paper, the authors evaluated the performance of structural features on the splice site prediction accuracy in Arabidopsis thaliana, where support vector machine (SVM) was employed as prediction algorithm, and the prediction accuracies of SVM, AdaBoost and XGBoost were observed to be at par and higher than that of RF and LogitBoost algorithms.

TL;DR: In this article , the authors evaluated ridge regression best linear unbiased prediction (rrBLUP) and various Bayesian models to evaluate genomic prediction accuracy using a 10-fold cross validation on 95 commercial and elite parental clones from the Louisiana sugarcane breeding program.