Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Fractionation in Triad Comparisons

Abstract: A statistical procedure for comparing the performance of a new product with the existing products on the basis of sensory characters has been developed A test statistic was evolved for testing the null hypothesis of equality of treatment effects in the case of fractional triad comparisons The null distribution of the test statistic has been obtained and it is found that it has a χ2-distribution for large number of observations The procedure is quite simple and is based on a distribution-free test requiring only ordinal scale measurement The method of analysis has been explained by a numerical example

On G‐Efficiency of some Second Order Rotatable Designs

Abstract: The effect of adding central points to a given design on the G-efficiency has been examined for certain classes of second order rotatable designs.

Augmented partial diallel design for estimating combining ability of parents

Abstract: The Augmented Partial Diallel Cross (APDC) represents an intermediate position between the Complete Diallel Cross (CDC) and the Partial Diallel Cross (PDC) in which one or more primary lines are crossed with all the other lines but the lines of secondary interest form a PDC system. The method of sampling adopted for crosses of secondary lines is from arrangement of secondary lines on circumference of a circle. The mathematics for analysis of such APDC has been given systematically. The efficiency of estimates of general combining ability (g.c.a.) effects obtained from APDC has been compared with that of Pederson's estimator and CDC. It is observed that there are four types of variances for g.c.a. effects where as for comparing specific combining ability (s.c.a.) effects there are large number of variances indicating that the design is totally unbalanced for s.c.a. comparisons.

RBPSpot: Learning on Appropriate Contextual Information for RBP Binding Sites Discovery

Abstract: Identifying RBP binding sites and mechanistic factors determining the interactions remain a big challenge. Besides the sparse binding motifs across the RNAs, it also requires a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites while using an ultra-fast BWT/FM-indexing coupled inexact k-mer spectrum search for statistically significant seeds. The seed works as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network (DNN). Contextual features based on pentamers/dinucloetides which also capture shape and structure properties appeared critical. Contextual CG distribution pattern appeared important. The developed models also got support from MD-simulation studies and the implemented software, RBPSpot, scored consistently high for the considered performance metrics including average accuracy of ∼90% across a large number of validated datasets while maintaining consistency. It clearly outperformed some recently developed tools, including some with much complex deep-learning models, during a highly comprehensive bench-marking process involving three different data-sets and more than 50 RBPs. RBPSpot, has been made freely available, covering most of the human RBPs for which sufficient CLIP-seq data is available (131 RBPs). Besides identifying RBP binding spots across RNAs in human system, it can also be used to build new models by user provided data for any species and any RBP, making it a valuable resource in the area of regulatory system studies.