Dr. Prabina Kumar Meher


Dr. Prabina Kumar Meher


S. No.Project  titleProject leader* and associatesFunding AgencyDuration
1Development of Machine learning models and Bayesian network for discovery of Nucleic acid-binding protein and their application in disease/pest surveillanceU.K. Pradhan*, Samarendra Das and P.K. MeherICAR-IASRI25.11.2021 – 24.07.2024
2Genomic prediction for micro-nutritional traits in bread wheat: A study on machine learning algorithmsPrabina Kumar Meher*ICAR (under Lal Bahadur Shasrtri Outstanding Young Scientist Award)01.04.2022 – 31.03.2025


S. No.Project titleProject leader* and associatesFunding AgencyDuration
1Development of statistical approach for prediction of eukaryotic splice sites Prabina Kumar Meher*,
S.D. Wahi and A. R. Rao
ICAR-IASRI, New Delhi03.09.13-02.09.15
2Estimation of Breeding Value Using Longitudinal Data (2016)*U.K. Pradhan, P.K. Meher, A.R. Rao and A.K. PaulICAR-IASRI, New Delhi23.04.14-22.10.16
3A study on sequence encoding based approaches for splice site prediction in agricultural species*P.K. Meher, Prakash Kumar and A.R. RaoICAR-IASRI, New Delhi01.01.16-27.10.18
4Gene selection for classification of gene expression data (Principal Investigator from 10.08.2017)Samarendra Das*, P.K. Meher, R.K. Paul and U.K. PradhanICAR-IASRI, New Delhi20.10.15-31.05.19
5Creating a fully characterised genetic resource pipelines for mustard improvement programme in India PAU: S.S. Banga*
ICAR-IARI: D.K. Yadav*
GBPUAT: Ram Bhajan*
ICAR-DRMR: K.H. Singh*
ICAR-IASRI: A.R. Rao*, Cini Varghese, P.K. Meher
National Agricultural Science Fund (NASF)01.01.17-31.12.19
6Studying drought-responsive genes in subtropical maize germplasm and their utility in development of tolerant maize hybrids ICAR-IARI: T. Nepolean*, M.G. Mallikarjuna and S. Jha
ICAR-IASRI: A.R. Rao* and P.K. Meher
ICAR (Under network project on computational biology and agricultural bioinformatics, through CABin, ICAR-IASRI)26.11.15-31.03.17
7Elucidating the mechanism of Pashmina fibre development: An OMICS approachSKUAST-K: Nazir A Ganai*
ICAR-IASRI: A.R. Rao* and P.K. Meher
National Agricultural Science Fund (NASF)01.07.15-30.06.18
8Studying drought-responsive genes in subtropical maize germplasm and their utility in development of tolerant maize hybrids ICAR-IARI: T. Viswanathan C* and M.G. Mallikarjuna
ICAR-IASRI: Anil Rai*, A.R. Rao and P.K. Meher
ICAR (Under network project on computational biology and agricultural bioinformatics, through CABin, IASRI)08.06.18-31.03.20
9Statistical approaches for genome-wide association studies and genomic selection for multiple traits in structured plant and animal population. L.M. Bhar*, P.K. Meher, H.S. RoyDepartment of Science and Technology (DST), Ministry of Science and Technology, Govt. Of India04.05.18-15.03.21




Senior Scientist

  1. G.R. Seth Memorial Young Scientist Award from Indian Society of Agricultural Statistics, 2016.
  2. MN Das Memorial Young Scientist appreciation certificate from Society of Statistics, Computers and Applications for the year 2016-17.
  3. Nehru Memorial Gold Medal from Indian Agricultural Statistics Research Institute, 2010.
  4. V.R. Murthy Award from Indian Agricultural Statistics Research Institute, 2010.
  5. ICAR-NET, Indian Council of Agriculture Research, Ministry of Agriculture, Govt. of India.  2010
  6. ICAR Senior Research Fellowship (2009-2010).
  7. ICAR Junior Research Fellowship (2007-2009).
  8. INSPIRE fellowship from Department of Science and Technology (DST), Ministry of Science and Technology, Government of India (2009-2012).
  9. Associate Fellow of National Academy of Agricultural Science (NAAS), New Delhi. 2022
  10. Selected Member of National Academy of Science (NASI), India. 2022
  11. Lal Bahadur Shastri Outstanding Young Scientist Award, Indian Council of Agricultural Research , 2022


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


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


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


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


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


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 Biotech11(11): 1-13. http://krishi.icar.gov.in/jspui/handle/123456789/68726


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


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


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


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 Reports9(1): 1-16. http://krishi.icar.gov.in/jspui/handle/123456789/73724


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


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


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


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


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


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


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


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


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


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


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


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


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 taurusScientific Reports, 10(1),1-13. http://krishi.icar.gov.in/jspui/handle/123456789/73744


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


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


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


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


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


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


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 Breeding41(7), pp.1-21. http://krishi.icar.gov.in/jspui/handle/123456789/68729


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


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


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


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


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


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 Science107, p.103517. http://krishi.icar.gov.in/jspui/handle/123456789/73734


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.

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