biblio

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2009
A. J. Kulkarni, Jayaraman, V. K., and Kulkarni, B. D., “Review on lazy learning regressors and their applications in QSAR”, Combinatorial Chemistry & High Throughput Screening, vol. 12, no. 4, pp. 440-450, 2009.
2015
M. Karthikeyan, Pandit, D., Bhavasar, A., and Vyas, R., “Design and development of cheminfocloud: an integrated cloud enabled platform for virtual screening”, Combinatorial Chemistry & High Throughput Screening, vol. 18, no. 6, pp. 604-619, 2015.
R. Vyas, Bapat, S., Jain, E., Tambe, S. S., Karthikeyan, M., and Kulkarni, B. D., “Study of applications of machine learning based classification methods for virtual screening of lead molecules”, Combinatorial Chemistry & High Throughput Screening, vol. 18, no. 7, pp. 658-672, 2015.
2018
R. Vyas, Bapat, S., Goel, P., Karthikeyan, M., Tambe, S. S., and Kulkarni, B. D., “Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data”, IEEE-ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 1, pp. 27-37, 2018.
2021
N. K. Gaur, Goyal, V. Durani, Kulkarni, K., and Makde, R. D., “Machine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics”, Bioorganic & Medicinal Chemistry Letters, vol. 38, p. 127852, 2021.
2022
G. Panditrao, Bhowmick, R., Meena, C., and Sarkar, R. Rup, “Emerging landscape of molecular interaction networks: opportunities, challenges and prospects”, Journal of Biosciences, vol. 47, no. 2, p. 24, 2022.
S. Agarwal and Joshi, K., “Looking beyond adsorption energies to understand interactions at surface using machine learning”, ChemistrySelect, vol. 7, no. 39, p. e202202414, 2022.
P. Singh, Ujjainiya, R., Prakash, S., Naushin, S., Sardana, V., Bhatheja, N., Singh, A. Pratap, Barman, J., Kumar, K., Gayali, S., Khan, R., Rawat, B. Singh, Tallapaka, K. Bharadwaj, Anumalla, M., Lahiri, A., Kar, S., Bhosale, V., Srivastava, M., Mugale, M. Nilakanth, Pandey, C. P., Khan, S., Katiyar, S., Raj, D., Ishteyaque, S., Khanka, S., Rani, A., Promila,, Sharma, J., Seth, A., Dutta, M., Saurabh, N., Veerapandian, M., Venkatachalam, G., Bansal, D., Gupta, D., Halami, P. M., Peddha, M. Serva, Veeranna, R. P., Pal, A., Singh, R. Kumar, Anandasadagopan, S. Kumar, Karuppanan, P., Rahman, S. Nasar, Selvakumar, G., Venkatesan, S., Karmakar, M. Kumar, Sardana, H. Kumar, Kothari, A., Parihar, D. Singh, Thakur, A., Saifi, A., Gupta, N., Singh, Y., Reddu, R., Gautam, R., Mishra, A., Mishra, A., Gogeri, I., Rayasam, G., Padwad, Y., Patial, V., Hallan, V., Singh, D., Tirpude, N., Chakrabarti, P., Maity, S. Krishna, Ganguly, D., Sistla, R., Balthu, N. Kumar, Kumar, K. A., Ranjith, S., B. Kumar, V., Jamwal, P. Singh, Wali, A., Ahmed, S., Chouhan, R., Gandhi, S. G., Sharma, N., Rai, G., Irshad, F., Jamwal, V. Lakshmi, Paddar, M. Ahmad, Khan, S. Ullah, Malik, F., Ghosh, D., Thakkar, G., Barik, S. K., Tripathi, P., Satija, Y. Kumar, Mohanty, S., Khan, M. Tauseef, Subudhi, U., Sen, P., Kumar, R., Bhardwaj, A., Gupta, P., Sharma, D., Tuli, A., Chaudhuri, S. Ray, Krishnamurthi, S., Prakash, L., V. Rao, C., Singh, B. N., Chaurasiya, A., Chaurasiya, M., Bhadange, M., Likhitkar, B., Mohite, S., Patil, Y., Kulkarni, M., Joshi, R., Pandya, V., Mahajan, S., Patil, A., Samson, R., Vare, T., Dharne, M., Giri, A., Mahajan, S., Paranjape, S., G. Sastry, N., Kalita, J., Phukan, T., Manna, P., Romi, W., Bharali, P., Ozah, D., Sahu, R. K., Dutta, P., Singh, M. Goutam, Gogoi, G., Tapadar, Y. Begam, Babu, E. V. S. S. K., Sukumaran, R. K., Nair, A. R., Puthiyamadam, A., Valappil, P. Kooloth, Prasannakumari, A. Velayudhan, Chodankar, K., Damare, S., Agrawal, V. Varun, Chaudhary, K., Agrawal, A., Sengupta, S., and Dash, D., “Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys”, Computers in Biology and Medicine, vol. 146, p. 105419, 2022.
R. Ben Ayed, Moreau, F., Ben Hlima, H., Rebai, A., Ercisli, S., Kadoo, N., Hanana, M., Assouguem, A., Ullah, R., and Ali, E. A., “SNP discovery and structural insights into OeFAD2 unravelling high oleic/linoleic ratio in olive oil”, Computational and Structural Biotechnology Journal, vol. 20, pp. 1229-1243, 2022.