Batra, U.*, Nathany, S.*, Kaushik Nath, S.*, T. Jose, J., Sharma, T., Preeti, P., Pasricha, S., Sharma, M., Arambam, N., Khanna, V., Bansal, A., Mehta, A., Rawal, K.* 'AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data,' The Lancet Regional Health: Southeast Asia. (in press)
*These authors contributed equally to this work.
Batra, U., Nathany, S., Nath, S.K., Jose, J.T., Sinha, R., P, P., Sharma, T., Pasricha, S., Sharma, M., Bansal, A. and Rawal, K., 2022. AI in NSCLC: PET-CT & histology model.
Impact Factor: 50.739
Nath, S.K., Pankajakshan, P., Sharma, T., Kumari, P., Shinde, S., Garg, N., Mathur, K., Arambam, N., Harjani, D., Raj, M. and Kwatra, G., 2023. A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets. Vaccines, 11(2), p.267.Nathany, S., Batra, U., Sharma, M., Jose, J.T., Nath, S.K., Sharma, T., Mehta, A. and Rawal, K., 2022. 257MO Integrating AI and ML with lung cancer diagnostics: A step ahead. Annals of Oncology, 33, p.S1533.
Impact Factor: 32.976
Rawal, K., Sinha, R., Nath, S.K., Preeti, P., Kumari, P., Gupta, S., Sharma, T., Strych, U., Hotez, P. and Bottazzi, M.E., 2022. Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates. Computers in biology and medicine, 145, p.105401.
Impact Factor: 6.698
Preeti P, Ankan Mukherjee Das, Prabhat Kumar, Ajay Gogia, Lalit Kumar, SVS Deo, Sundeep Mathur, Rajiv Janardan, Kamal Rawal, Manoj Garg, Bhudev C Das, (Preprint). Mutational profiling of the triple-negative breast cancer from an Indian cohort using whole exome sequencing.
Mandal, P.K., Rawal, K., Ramaswamy, R., Bhattacharya, A. and Bhattacharya, S., 2006. Identification of insertion hot spots for non-LTR retrotransposons: computational and biochemical application to Entamoeba histolytica. Nucleic acids research, 34(20), pp.5752-5763.
Impact Factor: 19.16
Rawal, K. and Ramaswamy, R., 2011. Genome-wide analysis of mobile genetic element insertion sites. Nucleic acids research, 39(16), pp.6864-6878.
Impact Factor: 19.16
Nath, S.K., Pankajakshan, P., Sharma, T., Kumari, P., Shinde, S., Garg, N., Mathur, K., Arambam, N., Harjani, D., Raj, M. and Kwatra, G., 2023. A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets. Vaccines, 11(2), p.267.
Impact Factor: 7.8
Jagannadham, J., Jaiswal, H.K., Agawal, S. and Rawal, K., 2015. Biomedical Text Mining of Obesity, Diabetes and Hypertension Genes. International Journal of Pharmaceutical Sciences Review and Research, 33(2), pp.182-186.
Impact Factor: 6.857
Jagannadham, J., Jaiswal, H.K. and Rawal, K., 2015. Deciphering relationships in disease networks using computational approaches: Fatty Liver, PCOD, Osteoarthritis, cholelithiasis & hyperlipdemia. International Journal of PharmTech Research, 8(1), pp.127-134.
Impact Factor: 6.05
Rawal, K., Sinha, R., Abbasi, B.A., Chaudhary, A., Nath, S.K., Kumari, P., Preeti, P., Saraf, D., Singh, S., Mishra, K. and Gupta, P., 2021. Identification of vaccine targets in pathogens and design of a vaccine using computational approaches. Scientific reports, 11(1), p.17626.
Impact Factor: 4.6
Rustagi, Y., Jaiswal, H.K., Rawal, K., Kundu, G.C. and Rani, V., 2015. Comparative characterization of cardiac development specific microRNAs: fetal regulators for future. PLoS ONE, 10(10), p.e0139359.
Impact Factor: 3.752
Jagannadham, J., Jaiswal, H.K., Agrawal, S., Rawal, K., 2016. Comprehensive Map of Molecules Implicated in Obesity. PLoS ONE 11(2): e0146759.
Impact Factor: 3.752
Abbasi, B.A., Saraf, D., Sharma, T., Sinha, R., Singh, S., Sood, S., Gupta, P., Gupta, A., Mishra, K., Kumari, P. and Rawal, K., 2022. Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches. PeerJ, 10, p.e13380.
Impact Factor: 3.06
Bakre, A.A., Rawal, K., Ramaswamy, R., Bhattacharya, A. and Bhattacharya, S., 2005. The LINEs and SINEs of Entamoeba histolytica: comparative analysis and genomic distribution. Experimental parasitology, 110(3), pp.207-213.
Impact Factor: 2.132
Dev, B.B., Malik, A. and Rawal, K., 2012. Detecting motifs and patterns at mobile genetic element insertion site. Bioinformation, 8(16), p.777.
Impact Factor: 1.9
Gupta, R., Soni, N., Patnaik, P., Sood, I., Singh, R., Rawal, K. and Rani, V., 2010. High AU content: a signature of upregulated miRNA in cardiac diseases. Bioinformation, 5(3), p.132.
Impact Factor: 1.9
Jethani, B., Gupta, M., Wadhwani, P., Thomas, R., Balakrishnan, T., Mathew, G., Mathur, M., Rao, B.P., Shukla, D., Khullar, A. and Khera, M., 2021. Clinical characteristics and remedy profiles of patients with COVID-19: a retrospective cohort study. Homeopathy, 110(02), pp.086-093.
Impact Factor: 1.818
Preeti P, Azeen Riyaz, Alakto Choudhury, Priyanka Ray Choudhury, Chaitanya Raghav, Nischal, Vrinda, Swarsat, Gayatri, Simrun, Abhijeet, Chhavi, Abhishek, Ishita, Jyotsana, Abhishek, Soundharya Kumaresan, Akanksha, Prashant, Kamal Rawal. DNASCANNER v2: a web-based tool to analyse nucleotide sequences for physicochemical and structural properties. Journal of Computational Biology (in press)
Impact Factor: 1.7
Preeti, P., Sinha, R. and Rawal, K., 2023. Identification and characterization of mobile genetic elements in high-throughput sequencing data using a computational approach. AIP Conference Proceedings [Preprint].
Impact Factor: 0.189
Sharma, P., Kumar, K. and Rawal, K., 2022. An In-Silico Approach for Analysing the interplay of Hepatitis B viral X protein with Human Adaptin protein. Journal of Research in Pharmacy, 26, pp.483-493.
Rawal, K., Priya, A., Malik, A., Bahl, R. and Ramaswamy, R., 2012. Distribution of MGEs and their insertion sites in the Macaca mulatta genome. Mobile Genetic Elements, 2(3), pp.133-141.
Impact Factor: 0.00
Rawal, K., Dorji, S., Kumar, A., Ganguly, A. and Grewal, A.S., 2013. Identification and characterization of MGEs and their insertion sites in the gorilla genome. Mobile Genetic Elements, 3(4), p.e25675.
Impact Factor: 0.00
Jain, S., Preeti, P. and Rawal, K., 2022. Evaluation of PcsB as Potential Vaccine Candidate Using Computational Tools. Journal of Immunology and Allergy, 3(1), pp.1-9.
: Prajapati A., Rawal, K., Preeti, P., 2023. Machine Learning Approach to Identify Receptor Binding Domain of Spike Glycoprotein as A Potential Vaccine Candidate for COVID-19. Journal of Immunology and Allergy. 2023;4(1):25-33.
Preeti, P., Nath, S.K., Arambam, N., Sharma, T., Choudhury, P.R., Choudhury, A., Khanna, V., Strych, U., Hotez, P.J., Bottazzi, M.E. and Rawal, K., 2023. Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development. Computational Vaccine Design(pp. 305-316). New York, NY: Springer US.