Dr. Upendra Kumar Pradhan


Dr. Upendra Kumar Pradhan


S.No.Project titleProject leader and associatesFundingDuration
1Development of Machine learning models and Bayesian network for discovery of Nucleic acid-binding protein and their application in disease/pest surveillance. Mr. U.K. Pradhan*
Dr. P.K. Meher
Institute25.11. 2021 to 24.05. 2024
2Statistical Approaches for Analysis of Zero-inflated and Over-dispersed Counts Data and their Applications in Single-cell StudiesMr. U.K. Pradhan*
Dr. S. Srivastava
Mr. Prakash Kumar
Institute25.11. 2021 to 24.05. 2024
3Potential irrigated area mapping through remotely sensed high resolution data.Dr. R. K. Jena
Dr. R. R. Sethi
Dr. Nirmal Kumar
Dr. S. Khedikar
Mr. U. K. Pradhan*
ICAR-IIWM05.09.2021 to 04.09.2024


S.No.Project titleProject leader and associatesFundingDuration
1Estimation of Breeding Value Using Longitudinal Data *U.K. Pradhan, P.K. Meher, A.R. Rao and A.K. PaulInstitute23.04.2014 to 31.12.2016
2A study on sequence encoding-based approaches for splice site prediction in agricultural species P.K. Meher*, U.K. Pradhan, S.D. Wahi and A.R. RaoICAR-IASRI, New Delhi01.01.16-27.10.18
3Gene 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





  • August 2009-July 2011: Awarded Junior Research Fellowship (JRF) by Indian Council  Agricultural Research (ICAR).
  • Qualified National Eligibility Test conducted by ICAR in 2011.

1.       Das S, Pradhan U, Rai SN. (2022) Five Years of Gene Networks Modeling in Single-cell RNA-sequencing Studies: Current Approaches and Outstanding Challenges. Current Bioinformatics. 17:1–1. http://krishi.icar.gov.in/jspui/handle/123456789/74201


2.      Jena RK, Bandyopadhyay S, Pradhan UK, Moharana PC, Kumar N, Sharma GK, Roy PD, Ghosh D, Ray P, Padua S, Ramachandran S, Das B, Singh SK, Ray SK, Alsuhaibani AM, Gaber A, Hossain A.(2022). Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties. Remote Sensing. 14(9):2101. http://krishi.icar.gov.in/jspui/handle/123456789/72092


3.      Moharana, P., Dharumarajan, S., Kumar, N., Pradhan, U., Jena, R., Naitam, R., Kumar, S., Singh, S., Meena, R., Nogiya, M., Meena, R., Tailor, B.(2022). Digital Mapping Algorithms to Estimate Soil Salinity in Indira Gandhi Nahar Pariyojana (IGNP) Command area of India. Agropedology.30:113–124. http://krishi.icar.gov.in/jspui/handle/123456789/72089


4.      Moharana, P., Dharumarajan, s., Kumar, N., Jena, R., Pradhan, U., Meena, R, Sahoo, S., Nogiya, M., Kumar, S., Meena, Roshan, Tailor, B., Singh, Singhsar, Singh, Surendra, Dwivedi, B., (2022). Modelling and Prediction of Soil Organic Carbon using Digital Soil Mapping in the Thar Desert Region of India. Journal of the Indian Society of Soil Science, 70, 86–96. http://krishi.icar.gov.in/jspui/handle/123456789/72460


5.      Pradhan UK, Sharma NK, Kumar P, Kumar A, Gupta S, Shankar R (2021). miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles. PLoS ONE, 16(10): e0258550. http://krishi.icar.gov.in/jspui/handle/123456789/68632.


6.      Kumari, M., Pradhan, U.K., Joshi, R., Punia, A., Shankar.,R, Kumar,R.(2021). In-depth assembly of organ and development dissected Picrorhiza kurroa proteome map using mass spectrometry. BMC Plant Biol 21, 604. http://krishi.icar.gov.in/jspui/handle/123456789/68657.


7.      Sharma, N.K., Gupta, S., Kumar, P, Kumar, A, Pradhan, U.K and Shankar, R. (2021) RBPSpot: Deep Learning on Appropriate Contextual Information for RBP Binding Sites Discovery. iScience. 24(12). 103381. http://krishi.icar.gov.in/jspui/handle/123456789/68655


8.      Verma, A., Kumar, P., Soni, M.L., Pawar, N., Pradhan, U.K. and Kumar, S. (2021). Nutrient dynamics of three species under agroforestry system in arid western region of Rajasthan, India. Biological Agriculture and Horticulture. http://krishi.icar.gov.in/jspui/handle/123456789/68659.   


9.      Moharana,P.C., Jena,R.K., Pradhan,U.K., Nogiya,M., Tailor,B.L., Singh,R.S. and Singh,S.K(2020). Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India. Precision Agriculture, 21, 426-448. http://krishi.icar.gov.in/jspui/handle/123456789/44678


10.   Kour, S, Shitap,M.S,  Pradhan,U.K. and Vaishnav P.R.(2018). Forecasting of rice yield based on weather parameters in Kheda District of Gujarat, India. International Journal of Agricultural and Statistical Sciences14(2):611-615. http://krishi.icar.gov.in/jspui/handle/123456789/42332


11.   Kour,S., Pradhan, U.K., Patel,J.S. and Vaishnav,P.R.(2018). Comparative Study of Selection Indices Based on Different Weights in Forage Sorghum [Sorghum bicolor (L.) Moench]. Journal of Crop and Weed.14(1): 17-23. http://krishi.icar.gov.in/jspui/handle/123456789/42338


12.   Pradhan, U.K., Lal, K., Dash, S and Singh, K.N. (2017). Designs and Analysis of Mixture Experiments with Process Variable in smaller number of runs. Communication in Statistics: Theory and Methods46(1): 259-270. http://krishi.icar.gov.in/jspui/handle/123456789/42320


13.  Kour,S., Pradhan, U.K., Paul R.K. and Vaishnav,P.R.(2017). Forecasting of Pearl millet productivity in Gujarat under time series framework. Economic Affairs.62(1):121-127. http://krishi.icar.gov.in/jspui/handle/123456789/42333


14.   Pradhan, UK, Lal, K. and Gupta, VK. (2013). Optimum conditions for mixture experiments with process variable for the expected response with minimum variability. Statist. Appln., 10, 63-71.  http://krishi.icar.gov.in/jspui/handle/123456789/42319


15.  Das, S, Meher, P.K, Pradhan, U.K., 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, 112 (6), 1257- 1262. http://krishi.icar.gov.in/jspui/handle/123456789/43016


16.  Das, S, Paul, A.K., Wahi, S.D., Pradhan, U.K.(2017). Comparative performance of imputation methods for different proportions of missing data in classification of crop genotypes. Journal of the Indian Society of Agricultural Statistics, 71(2): 147–153. http://krishi.icar.gov.in/jspui/handle/123456789/43020


17.  Kour,S., Vaishnav,P.R., Behera, S.K. and Pradhan, U.K (2017). Statistical Modelling for Forecasting of Pearl Millet (Pennisetu glaucum) productivity Based on Weather parameter. Indian Journal of Ecology,44:33-37. http://krishi.icar.gov.in/jspui/handle/123456789/42336


18.  Kour, S., Pradhan, U.K (2016). Correlation, Path coefficient Analysis and construction of indices for yield and yield components selection in forage sorghum [Sorghum bicolor(L) Moench]. Journal of Crop and Weed,12(2):01-09. http://krishi.icar.gov.in/jspui/handle/123456789/42339


19.  Kour,S., Pradhan, U.K. (2016). Genetic variability, heritability and expected     genetic advance for yield and yield components in forage sorghum [Sorghum bicolor(L) Moench]. RASHI. 1(2):71-76. http://krishi.icar.gov.in/jspui/handle/123456789/42337



1.      Lal K., Pradhan U.K., Gupta V.K. (2020) Some Investigations on Designs for Mixture Experiments with Process Variable. Statistical Methods and Applications in Forestry and Environmental Sciences. Forum for Interdisciplinary Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-15-1476-0_12.

2.      Moharana P, Pradhan U, Jena R, Sahoo S, Meena R.(2022). Delineation of Irrigation Management Zones Using Geographical Weighted Principal Component Analysis and Possibilistic Fuzzy C-Means Clustering Approach. Soil Health and Environmental Sustainability, ISBN: 978-3-031-09269-5. Springer, Singapore. 10.1007/978-3-031-09270-1_10

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