Senior Scientist
Prabina[dot]meher[at]icar[dot]gov[dot]in
| Name | Dr. Prabina Kumar Meher |
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Prabina[dot]meher[at]icar[dot]gov[dot]in |
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| Designation | Senior Scientist |
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| Articles | 1 Meher, P.K., Begam, S., Sahu, T.K., Gupta, A., Kumar, A., Kumar, U., Rao, A.R., Singh, K.P. and Dhankher, O.P., 2022. ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features. International Journal of Molecular Sciences, 23(3), 1612. http://krishi.icar.gov.in/jspui/handle/123456789/72390 2 Meher, P.K., Rustgi, S. and Kumar, A., 2022. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results. Heredity, pp.1-12. https://doi.org/10.1038/s41437-022-00539-9.http://krishi.icar.gov.in/jspui/handle/123456789/72398 3 Meher, P.K., Dash, S., Sahu, T.K., Satpathy, S. and Pradhan, S.K. (2022) GIpred: A computational tool for prediction of GIGANTEA proteins using machine learning algorithm. Physiology and Molecular Biology of Plants. https://doi.org/10.1007/s12298-022-01130-6. http://krishi.icar.gov.in/jspui/handle/123456789/72379 4 Meher, P.K., Rai, A. and Rao, A.R., 2021. mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net. BMC Bioinformatics, 22(1): 1-24. http://krishi.icar.gov.in/jspui/handle/123456789/68725 5 Meher, P.K., Mohapatra, A., Satpathy, S., Sharma, A., Saini, I., Pradhan, S.K. and Rai, A., 2021. PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel. Plant Methods, 17(1): 1-15. http://krishi.icar.gov.in/jspui/handle/123456789/68728 6 Meher, P.K. and Satpathy, S., 2021. Improved recognition of splice sites in A. thaliana by incorporating secondary structure information into sequence-derived features: a computational study. 3 Biotech, 11(11): 1-13. http://krishi.icar.gov.in/jspui/handle/123456789/68726 7 Meher, P.K., Sahu, T.K., Mohanty, J., Gahoi, S., Purru, S., Grover, M. and Rao, A.R., 2018. nifPred: Proteome-wide identification and categorization of nitrogen-fixation proteins of diaztrophs based on composition-transition-distribution features using support vector machine. Frontiers in Microbiology, 9, 1100. http://krishi.icar.gov.in/jspui/handle/123456789/73682 8 Meher, P.K., Sahu,T.K., Saini, V. and Rao, A.R. (2017). Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Scientific Reports 7: 42362. http://krishi.icar.gov.in/jspui/handle/123456789/73709 9 Meher, P.K., Satpathy, S. and Rao, A.R. (2020). miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides. Scientific Reports, 10(1),1-12. http://krishi.icar.gov.in/jspui/handle/123456789/73716 10 Meher, P.K., Sahu, T.K., Raghunandan, K., Gahoi, S., Choudhury, N.K. and Rao, A.R., 2019. HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine. Scientific Reports, 9(1): 1-16. http://krishi.icar.gov.in/jspui/handle/123456789/73724 11 Meher P.K., Sahu T.K., Gahoi S., and Rao A.R. (2018). ir-HSP: Improved recognition of heat shock proteins, their families and sub-types based on g-spaced di-peptide features and support vector machine. Frontiers in Genetics, 8: 235. http://krishi.icar.gov.in/jspui/handle/123456789/73725 12 Meher P.K., Sahu TK., Banchariya A. and Rao AR. (2017). DIRProt: A computational approach for discriminating insecticide resistant proteins from non-resistant proteins. BMC Bioinformatics, 18: 190. http://krishi.icar.gov.in/jspui/handle/123456789/73726 13 Meher, P.K., Sahu, T.K., Rao, A.R. and Wahi, S.D. (2014). A statistical approach for 5’ splice site prediction using short sequence motifs and without encoding sequence data. BMC Bioinformatics, 15: 362. http://krishi.icar.gov.in/jspui/handle/123456789/73745 14 Meher, P.K., Sahu, T.K. and Rao, A.R. (2016). Prediction of donor splice sites using random forest with a new sequence encoding approach. BioData Mining, 9:4. http://krishi.icar.gov.in/jspui/handle/123456789/73727 15 Meher, P.K., Sahu, T.K., Rao, A.R., Wahi, S.D. (2016) Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features. Algorithms for Molecular Biology, 11:16. http://krishi.icar.gov.in/jspui/handle/123456789/73736 16 Meher, P.K., Sahu,T.K. and Rao, A.R. (2016) Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier. Gene, 592: 316–324. http://krishi.icar.gov.in/jspui/handle/123456789/73731 17 Meher, P.K., Sahu, T.K., Rao, A.R., Wahi, S.D. (2016) A computational approach for prediction of donor splice sites with improved accuracy. Journal of Theoretical Biology, 404: 285–294. http://krishi.icar.gov.in/jspui/handle/123456789/73728 18 Meher, P.K., Sahu, T.K., Gahoi, S., Tomar, R. and Rao, A.R., 2019. funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model. BMC Genetics, 20(1),1-13. http://krishi.icar.gov.in/jspui/handle/123456789/73730 19 Meher, P.K., Sahu, T.K., Rao, A.R. and Wahi, S.D. (2016). Discriminating coding from non-coding regions based on codon structure and methylation-mediated substitution: An application in rice and cattle. Computers and Electronics in Agriculture, 129: 66–73. http://krishi.icar.gov.in/jspui/handle/123456789/73733 20 Meher, P.K., Sahu, T.K., Gahoi, S., Satpathy, S. and Rao, A.R., 2019. Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition. Gene, 705, 113-126. http://krishi.icar.gov.in/jspui/handle/123456789/73739 21 Meher, P.K., Sahu, T.K., Rao, A.R. (2016) Performance evaluation of neural network, support vector machine and random forest for prediction of donor splice sites in rice. Indian Journal of Genetics, 76(2): 173-180. http://krishi.icar.gov.in/jspui/handle/123456789/73748 22 Meher, P.K., Sahu, T.K., Rao, A.R. and Wahi, S.D. (2015). Determination of window size and identification of suitable method for prediction of donor splice sites in rice (Oryza sativa) genome. Journal of Plant Biochemistry and Biotechnology, 24(4): 385-392. http://krishi.icar.gov.in/jspui/handle/123456789/73740 23 Sahu, T.K., Gurjar, A.K.S., Meher, P.K., Varghese, C., Marwaha, S., Rao, G.P., Rai, A., Guleria, N., Basagoudanavar, S.H., Sanyal, A. and Rao, A.R. (2020). Computational insights into RNAi-based therapeutics for foot and mouth disease of Bos taurus. Scientific Reports, 10(1),1-13. http://krishi.icar.gov.in/jspui/handle/123456789/73744 24 Srivastava, R., Bajaj, D., Sayal, Y.K., Meher, P.K., Upadhyaya, H.D., Kumar, R., Tripathi, S., Bharadwaj, C., Rao, A.R., Parida S.K. (2016) Genome-wide development and deployment of informativeintron-spanning and intron-length polymorphism markers for genomics-assisted breeding applications in chickpea. Plant Science, 252: 374–387. http://krishi.icar.gov.in/jspui/handle/123456789/73762 25 Das S, Meher P.K, Rai A, Bhar L.M and Mandal B.N. (2017). 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.). PLoS ONE 12(1): e0169605. http://krishi.icar.gov.in/jspui/handle/123456789/73742 26 Jaiswal, V., Gahlau,t V., Meher, P.K., Mir, R.R., Jaiswal, J.P., Rao, A.R., Balyan, H.S. and Gupta, P.K. (2016). Genome wide single locus single trait, multi-locus and multi-trait association mapping for some important agronomic traits in common wheat (T. aestivum l.). PLoS ONE, 11(7): e0159343. http://krishi.icar.gov.in/jspui/handle/123456789/73738 27 Sharma, D., Tiwari, A., Sood, S., Jamra, G., Singh, N.K., Meher, P.K. and Kumar, A., 2018. Genome wide association mapping of agro-morphological traits among a diverse collection of finger millet (Eleusine coracana L.) genotypes using SNP markers. PLoS ONE, 13(8), e0199444. http://krishi.icar.gov.in/jspui/handle/123456789/73741 28 Kumar, J., Saripalli, G., Gahlaut, V., Goel, N., Meher, P.K., Mishra, K.K., Mishra, P.C., Sehgal, D., Vikram, P., Sansaloni, C. and Singh, S., 2018. Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS. Euphytica, 214(11), 1-17. http://krishi.icar.gov.in/jspui/handle/123456789/73747 29 Navathe, S., Yadav, P.S., Chand, R., Mishra, V.K., Vasistha, N.K., Meher, P.K., Joshi, A.K. and Gupta, P.K., 2020. ToxA–Tsn1 interaction for spot blotch susceptibility in Indian wheat: An example of inverse gene-for-gene relationship. Plant Disease, 104(1), 71-81. http://krishi.icar.gov.in/jspui/handle/123456789/73737 30 Malik, P., Kumar, J., Singh, S., Sharma, S., Meher, P.K., Sharma, M.K., Roy, J.K., Sharma, P.K., Balyan, H.S., Gupta, P.K. and Sharma, S., 2021. Single-trait, multi-locus and multi-trait GWAS using four different models for yield traits in bread wheat. Molecular Breeding, 41(7), pp.1-21. http://krishi.icar.gov.in/jspui/handle/123456789/68729 31 Sarkar, R.K., Meher, P.K., Wahi, S.D., Mohapatra, T. and Rao, A.R. (2014). An approach to the development of a core set of germplasm using a mixture of qualitative and quantitative data. Plant Genetic Resource: Characterization and Utilization, 13(2): 96-103. http://krishi.icar.gov.in/jspui/handle/123456789/73760 32 Sarkar, R.K., Rao, A.R., Meher, P.K., Nepolean, T. and Mohapatra Evaluation of random forest regression for prediction of breeding value from genome-wide SNPs., T. (2015). Journal of Genetics, 94(2): 187-192. http://krishi.icar.gov.in/jspui/handle/123456789/73757 33 Das, S., Meher, P.K., Pradhan, U.K. and Paul, A.K., 2017. Inferring gene regulatory networks using Kendall’s tau correlation coefficient and identification of salinity stress responsive genes in rice. Current Science, 1257-1262. http://krishi.icar.gov.in/jspui/handle/123456789/73735 34 Sahu, T.K., Rao, A.R., Meher, P.K., Sahoo, B.C., Gupta, S. and Rai, A., 2015. Computational prediction of MHC class I epitopes for most common viral diseases in cattle (Bos taurus). Indian Journal of Biochemistry and Biophysics, 52: 34-44. http://krishi.icar.gov.in/jspui/handle/123456789/73732 35 Malik, P., Kumar, J., Sharma, S., Meher, P.K., Balyan, H.S., Gupta, P.K. and Sharma, S., 2022. GWAS for main effects and epistatic interactions for grain morphology traits in wheat. Physiology and Molecular Biology of Plants, 28(3), pp.651-668. http://krishi.icar.gov.in/jspui/handle/123456789/73729 36 Sharma, D., Tiwari, A., Sood, S., Meher, P.K. and Kumar, A., 2022. Identification and validation of candidate genes for high calcium content in finger millet [Eleusine coracana (L.) Gaertn.] through genome-wide association study. Journal of Cereal Science, 107, p.103517. http://krishi.icar.gov.in/jspui/handle/123456789/73734 |
| Books | Book Chapters 1. Gupta, S, Sahu, TK and Meher, PK (2014). System biology – An overview. Computational Biology and Bioinformatics, 6 (Biotechnology Series), Studium Press LLC, USA, 379-406, ISBN: 1-62699-015-8.
2. Sahu, TK, Singh, N, Meher, PK, Wahi, SD and Rao, AR (2014). Epigenetics in sustaining the livelihood. Computational Biology and Bioinformatics, 6 (Biotechnology series), ISBN: 1-62699-015-8, Studium Press LLC, USA. 437- 462.
3. Meher, P.K., Kumar, A., Pradhan, S.K. (2022). Genomic Selection Using Bayesian Methods: Models, Software, and Application. In: Wani, S.H., Kumar, A. (eds) Genomics of Cereal Crops. Springer Protocols Handbooks. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2533-0_13.
4. Kumari, M., Muduli, L., Meher, P.K., Pradhan, S.K. (2022). Genome-Wide Association Study (GWAS) for Trait Analysis in Crops. In: Wani, S.H., Kumar, A. (eds) Genomics of Cereal Crops. Springer Protocols Handbooks. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2533-0_15.
5. Kumar, A., Sharma, M., Gautam, T., Meher, P.K., Bhati, J., Avashthi, H., Budhlakoti, N., Mishra, D.C., Angadi, U.B. and Singh, K.P. (2022). Protocol for In Silico Identification and Functional Annotation of Abiotic Stress–Responsive MicroRNAs in Crop Plants. In: Wani, S.H., Kumar, A. (eds) Genomics of Cereal Crops. Springer Protocols Handbooks. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2533-0_9. |
