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Institution

Indian Agricultural Statistics Research Institute

FacilityNew 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
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Sunil Kumar302303194
Atmakuri Ramakrishna Rao211091803
Charanjit Kaur20804320
Anil Rai202081595
Ranjit Kumar Paul1793875
Hukum Chandra1775825
Sudhir Srivastava17691123
Krishan Lal16681022
Ashish Das151461218
Eldho Varghese15127842
Deepti Nigam1429812
Mir Asif Iquebal1488604
Rajender Parsad1398799
Deepak Singla1332422
Prem Narain1380503
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20232
202212
2021134
2020107
201951
201868