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
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TL;DR: In this paper, an attempt was made to assess the geoavailability of heavy metals in polluted soils using short sequential fractionation schemes and 25 composite soil samples were collected for this purpose.
Abstract: An attempt has been made to assess the geoavailability of heavy metals in polluted soils using short sequential fractionation schemes. For this purpose, 25 composite soil samples were collected fro...
11 citations
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TL;DR: This article proposes a linear integer programming-based algorithm to construct balanced incomplete block designs and demonstrates that the proposed algorithm is competitive with the existing algorithms.
Abstract: This article proposes a linear integer programming-based algorithm to construct balanced incomplete block designs. Working of the algorithm is illustrated with the help of an example. The algorithm is able to generate balanced incomplete block designs very fast in most of the cases. The performance of the proposed algorithm is compared with other algorithms proposed in the literature. It is demonstrated that the proposed algorithm is competitive with the existing algorithms.
11 citations
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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.
Abstract: MicroRNAs (miRNAs) are one kind of non-coding RNA, play vital role in regulating several physiological and developmental processes. Subcellular localization of miRNAs and their abundance in the native cell are central for maintaining physiological homeostasis. Besides, RNA silencing activity of miRNAs is also influenced by their localization and stability. Thus, development of computational method for subcellular localization prediction of miRNAs is desired. In this work, we have 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. Prediction accuracy was analyzed following fivefold cross validation, where ~ 63–71% of AUC-ROC and ~ 69–76% of AUC-PR were observed. While evaluated with independent test set, > 50% localizations were found to be correctly predicted. Besides, the developed computational model achieved higher accuracy than the existing methods. A user-friendly prediction server “miRNALoc” is freely accessible at http://cabgrid.res.in:8080/mirnaloc/
, by which the user can predict localizations of miRNAs.
11 citations
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TL;DR: In this paper, the authors investigated the use of hydrophilic polymer (Disco Clear) film coating as a tool for ensuring safe seed storage and found that the polymer film coat acted as a physical barrier for absorption of moisture in the vapour phase and hence, under ambient conditions, the film coated seeds remained effectively protected from equilibration with fluctuating relative humidity, in a manner comparable with storage in moisture impervious containers.
11 citations
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TL;DR: It may be concluded that multi-trait-based GS methods have great potential to increase genetic gain as they utilize the correlation among the response variable as extra information, which contributes to estimate breeding value more precisely.
Abstract: In recent years of animal and plant breeding research, genomic selection (GS) became a choice for selection of appropriate candidate for breeding as it significantly contributes to enhance the genetic gain. Various studies related to GS have been carried out in the recent past. These studies were mostly confined to single trait. Although GS methods based on single trait have not performed very well in cases like pleiotropy, missing data and when the trait under study has low heritability. Gradually, some studies were carried out to explore the possibility of methods for GS based on multiple traits in the view of overcoming the above-mentioned problems in the method of single-trait GS (STGS). Currently, multi-trait-based GS methods are getting importance as it exploits the information of correlated structure among response. In this study, we have compared various methods related to STGS, such as stepwise regression, ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian, best linear unbiased prediction, and support vector machine, and multi-trait-based GS methods, such as multivariate regression with covariance estimation, conditional Gaussian graphical models, mixed model, and LASSO. In almost all cases, multi-trait-based methods are found to be more accurate. Based on the results of this study, it may be concluded that multi-trait-based methods have great potential to increase genetic gain as they utilize the correlation among the response variable as extra information, which contributes to estimate breeding value more precisely. This study is a comprehensive review of the methods of GS right from single trait to multiple traits and comparisons among these two classes.
11 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 |