P
Prabina Kumar Meher
Researcher at Indian Agricultural Statistics Research Institute
Publications - 50
Citations - 797
Prabina Kumar Meher is an academic researcher from Indian Agricultural Statistics Research Institute. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 11, co-authored 35 publications receiving 509 citations. Previous affiliations of Prabina Kumar Meher include Indian Council of Agricultural Research.
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Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.
TL;DR: This study made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy, and achieved higher accuracy than several existing approaches, while compared using benchmark dataset.
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Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS
Jitendra Kumar,Gautam Saripalli,Vijay Gahlaut,Neha Goel,Prabina Kumar Meher,K. K. Mishra,Prafulla Chandra Mishra,Deepmala Sehgal,Prashant Vikram,Carolina Sansaloni,Sukhwinder Singh,Pradeep Sharma,Pushpendra Kumar Gupta +12 more
TL;DR: Nine most important MTAs were selected for biofortification and were associated with three traits (GPC, GFeC and GYPP), which can be used in wheat improvement programs either using marker-assisted recurrent selection or pseudo-backcrossing method.
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Genome Wide Single Locus Single Trait, Multi-Locus and Multi-Trait Association Mapping for Some Important Agronomic Traits in Common Wheat (T. aestivum L.).
Vandana Jaiswal,Vijay Gahlaut,Prabina Kumar Meher,Reyazul Rouf Mir,Jai Prakash Jaiswal,Atmakuri Ramakrishna Rao,Harindra Singh Balyan,Pushpendra Kumar Gupta +7 more
TL;DR: The epistatic interactions detected during the GWAS provided better insight into genetic architecture of the 14 traits that were examined during the present study, and the power of association mapping improved due to MLMM and MTMM analyses.
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Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean ( Glycine max L.)
TL;DR: The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes and revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.
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Genome wide association mapping of agro-morphological traits among a diverse collection of finger millet (Eleusine coracana L.) genotypes using SNP markers.
Divya Sharma,Apoorv Tiwari,Apoorv Tiwari,Salej Sood,Gautam Jamra,N. K. Singh,Prabina Kumar Meher,Anil Kumar +7 more
TL;DR: 109 novel SNPs associated with important agro-morphological traits, reported for the first time in this study, could be precisely utilized in finger millet genetic improvement after validation.