Latest Research & Development

ICAR-IASRI has developed a computational model for identifying genes responsive to six abiotic stresses: cold, drought, heat, light, oxidative, and salt. The predictions were performed using support vector machine (SVM), random forest, adaptive boosting (ADB), and extreme gradient boosting (XGB). The online prediction application, ASRpro, is made freely available ( ) for predicting abiotic SRGs and proteins.

R-packages Developed :

  •  iRoCoDe: To generate row-column designs with incomplete rows and columns, by amalgamating two incomplete block designs (D1 and D2). The selection of D1 and D2 (the input designs) can be done from the available incomplete block designs, viz., balanced incomplete block designs/ partially balanced incomplete block designs/ t-designs.
  • AutoWeatherIndices: This package provides the user with weather indices from the weather variables. The obtained weather indices are crucial inputs for further implementation of any statistical tools such as regression analysis, time series models or any machine learning algorithm in regression framework.
  • VMDML: Variational Mode Decomposition Based Machine Learning Models. This package has implemented four different variant of Variational Mode decomposition based univariate models namely VMDARIMA, VMDRF, VMDSVR and VMDTDNN.

Biological-database/software-tools Developed

  • Database Developed: TiGeR: Tilletia indica genomic resource freely accessible at .
  • Software-Tool: DeepAProt: Abiotic stress protein classification tool using Deep Learning in cereal: A web-based tool that helps the biologist to classify the unknown protein sequence to the respective class of abiotic stress. This web-server is freely accessible at .

Research articles published in referred journals:

  1. Sarmah, M, Borgohain, A, Gogoi, BB, Yeasin, M, Paul, RK, Malakar, H, Handique, GJ, Saikia, J, Deka, D, Khare, P, and Karak, T (2022). Insights into the Effects of Tea Pruning Litter Biochar on Major Micronutrients (Cu, Mn, and Zn) Pathway From Soil to Tea Plant: An Environmental Armour. Journal of Hazardous Materials, 129970,
  2. Rathore, N, Kumar, P, Mehta, N, Swarnkar, MK, Shankar, R and Chawla, A (2022). Time-series RNA-Seq transcriptome profiling reveals novel insights about cold acclimation and de-acclimation processes in an evergreen shrub of high altitude. Scientific Reports, 12, 15553.
  3. Meher, PK, Sahu, TK, Gupta, A, Kumar, A and Rustgi, S (2022). ASRpro: A machine‐learning computational model for identifying proteins associated with multiple abiotic stress in plants. The Plant Genome, p.e20259.
  4. Pratap, V, Dass, A, Dhar, S, Babu, S, Singh, VK, Singh, R, Krishnan, P, Sudhishri, S, Bhatia, A, Kumar, S, Choudhary, AK, Singh, R, Kumar, P, Sarkar, SK, Verma, SK, Kumari, K, San, AA (2022). Co-Implementation of Tillage, Precision Nitrogen, and Water Management Enhances Water Productivity, Economic Returns, and Energy-Use Efficiency of Direct Seeded Rice. Sustainability, 14, 11234. su141811234. /jspui /handle/123456789/74091
  5. Tiwari, D, Murmu, S, Indari, O, Jha, HC and Kumar, S (2022) Targeting Epstein-Barr virus dUTPase, an immunomodulatory protein using anti-viral, anti-inflammatory and neuroprotective phytochemicals. Chemical Biodiversity ;19(9):e202200527.
  6. Tanwy, D, Mishra, DC * and Rai, A (2022). Role of Bioinformatics in the Development of Plant Genetic Resources. Indian Journal of Plant Genetic Resources 35(3): 200–203 (2022) DOI 10.5958/0976-1926.2022.00069.9.
  7. Sonkusale, L, Chaturvedi, KK, Lal, SB, Farooqi, MS, Sharma, A, Joshi, P, Lama, A and Mishra, DC (2022). Exploring the Applicability of Topic Modeling in SARS-CoV-2 Literature and Impact on Agriculture. Indian Research Journal Extension Education. 22 (4).
  8. Pathak, J, Ramasamy, GG, Agrawal, A, Srivastava, S, Basavaarya, BR, Muthugounder, M, Muniyappa, VK, Maria, P, Rai, A and Venkatesan, T (2022). Comparative Transcriptome Analysis to Reveal Differentially Expressed Cytochrome P450 in Response to Imidacloprid in the Aphid Lion, Chrysoperla zastrowi sillemi (Esben-Petersen). Insects 2022, 13, 900.
  9. Parihar, AK, Gupta, S, Hazra, KK, Lamichaney, A, Gupta, DS, Singh, D, Kumar, R,  Singh, AK,  Vaishnavi, R,  Muniyandi, SJ,  Das, SP,  Sharma, JD, Yadav, RK,  Jamwal, BS, Choudhary, BR, Khedar, OP, Prakash, V, Dikshit, HK, Panwar, RK,  Kumar, M, Kumar, P, Mahto, CS,  Borah, HK, Singh, MN, Das, A, Patil, AN,  Nanda, HC, Kumar, V, Rajput, SS, Chauhan, DA, Patel, MH, Kanwar, R, Kumar, J, Mishra, SP, Kumar, H, Swarup, I, Mogali, SC,  Kumaresan, D, Manivannan N, Byregowda, M, Muthaiyan, P, Rao, PJM,  Shivani, D,  Prusti, AM,  Mahadevu, P,   Iyanar, K, Das, S (2022). Multi-location evaluation of mungbean (Vigna radiata L.) in Indian climates: eco-phenological dynamics, yield relation and characterization of locations. Frontiers in Plant Science. DOI=10.3389/fpls.2022.984912.
  10. Jaiswal, R, Choudhary, K and Kumar, RR (2022) STL‑ELM: A Decomposition‑Based Hybrid Model for Price Forecasting of Agricultural Commodities, National Academy Science Letters.
  11. Lakshmi, S, Chaturvedi, KK, Lal, SB, Farooqi, MS, Sharma, A, Joshi, P, Lama, A and Mishra, DC (2022). Exploring the Applicability of Topic Modeling in SARS-CoV-2 Literature and Impact on Agriculture, Indian Research Journal of Extension Education, 22(4): 48-56.
  12. Thankchen, J, Iyer, R, Gupta, K, Azmi, FT & Ray, M (2022). Relationship between employee resilience and work role performance in higher education. Positif Journal, 22(9): 138-153.



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