Institution
Indian Agricultural Statistics Research Institute
Facility•New Delhi, India•
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: This is first report of muscle transcriptome depicting candidate genes with GRN controlling FCE and ADG, and reported putative molecular markers, candidate genes and hub proteins can be valuable genomic resources for association studies in genetic improvement programme.
13 citations
TL;DR: Production of inulin from yam bean tubers by ultrasonic assisted extraction (UAE) was optimized by using response surface methodology (RSM) and genetic algorithms (GA) and UAE provided a shade better purity of extracted inulin than other two techniques.
13 citations
TL;DR: The RF methodology can be used as an alternative to the model-based techniques for the prediction of breeding value at genome level with higher accuracy, according to the correlations between the predicted and observed trait response.
Abstract: Genomic prediction is meant for estimating the breeding value using molecular marker data which has turned out to be a powerful tool for efficient utilization of germplasm resources and rapid improvement of cultivars. Model-based techniques have been widely used for prediction of breeding values of genotypes from genomewide association studies. However, application of the random forest (RF), a model-free ensemble learning method, is not widely used for prediction. In this study, the optimum values of tuning parameters of RF have been identified and applied to predict the breeding value of genotypes based on genomewide single-nucleotide polymorphisms (SNPs), where the number of SNPs (P variables) is much higher than the number of genotypes (n observations) (P > > n). Further, a comparison was made with the model-based genomic prediction methods, namely, least absolute shrinkage and selection operator (LASSO), ridge regression (RR) and elastic net (EN) under P > > n. It was found that the correlations between the predicted and observed trait response were 0.591, 0.539, 0.431 and 0.587 for RF, LASSO, RR and EN, respectively, which implies superiority of the RF over the model-based techniques in genomic prediction. Hence, we suggest that the RF methodology can be used as an alternative to the model-based techniques for the prediction of breeding value at genome level with higher accuracy.
13 citations
TL;DR: In this paper, the standard approach for small area estimation (SAE) based on linear mixed models often yields inefficient estimates for skewed data, and Chandra and Chambers (2011a) described SAE for skewed datasets.
Abstract: The standard approach for small area estimation (SAE) based on linear mixed models often yields inefficient estimates for skewed data. Chandra and Chambers (2011a) described SAE for skewed data tha...
13 citations
TL;DR: This article identified 21 novel MAPKs through gel-based proteomics and RNA-seq data analysis based on digital gene expression, two transcripts (transcript_2834 and transcript_8242) showing homology with MAPK were cloned and characterized from wheat (acc nos MK854806 and KT835664).
13 citations
Authors
Showing all 462 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Sunil Kumar | 30 | 230 | 3194 |
| Atmakuri Ramakrishna Rao | 21 | 109 | 1803 |
| Charanjit Kaur | 20 | 80 | 4320 |
| Anil Rai | 20 | 208 | 1595 |
| Ranjit Kumar Paul | 17 | 93 | 875 |
| Hukum Chandra | 17 | 75 | 825 |
| Sudhir Srivastava | 17 | 69 | 1123 |
| Krishan Lal | 16 | 68 | 1022 |
| Ashish Das | 15 | 146 | 1218 |
| Eldho Varghese | 15 | 127 | 842 |
| Deepti Nigam | 14 | 29 | 812 |
| Mir Asif Iquebal | 14 | 88 | 604 |
| Rajender Parsad | 13 | 98 | 799 |
| Deepak Singla | 13 | 32 | 422 |
| Prem Narain | 13 | 80 | 503 |