Contribution to Science and Current Research
  1. Built a first integrated pipeline & machine learning based resource for analysis of mobile genetic elements and mutations in cancers and other diseases
    1. Rawal, K. and Ramaswamy, R., "Genome wide analysis of mobile genetic elements insertion sites. Nucl. Acids Res.,vol. 39, no. 16, pp. 6864-6878, Sep. 2011. Impact Factor 11.3.
    2. Mandal, P., Rawal, K., Ramaswamy, R., Bhattacharya, A. and Bhattacharya, S. "Identification of Insertion hot spots for non-LTR retrotransposons: Computational and Biochemical application to Entamoeba histolytica." Nucl. Acids Res., vol. 34, no. 20, pp. 5752-5763, 2006. (Lead author and equal contribution). Impact Factor 11.3.
    3. Dev, B.B., Malik A., Rawal, K., “Detecting motifs and patterns at mobile genetic element insertion site”. Bioinformation, vol. 8, pp.777-786, 2012.
    4. Rawal, K., Dorji, S. Kumar, A., Ganguly, A. Grewal, A.S. “Identification and characterization of MGEs and their insertion sites in the gorilla genome”. Mobile Genetic Elements, vol.3, no.4, pp. e25675, 2012.
    5. Rawal, K., Priya, A., Malik, A., Bahl, R., Ramaswamy, R., “Distribution of MGEs and their insertion sites in the Macaca mulatta genome”. Mobile Genetic Elements, vol.2, no.3, pp. 133-141, 2012
    6. Bakre, A.A.,Rawal, K., Ramaswamy, R., Bhattacharya, A. and Bhattacharya, S., “The LINEs and SINEs of Entamoeba histolytica: Comparative analysis and genomic distribution.” Experimental Parasitology, vol. 110, no. 3, pp. 207-213, 2005.
  2. Developed the first molecular network on human obesity through screening >25 million pubmed records, gene expression databases, clinical studies, drug side effects and other information resources. Built a new text mining system and machine learning model for semi-automated screening of literature records with high F score.
    1. Jagannadham, J., Jaiswal, H.K., Agrawal, S., Rawal, K., Comprehensive map of molecules implicated in obesity", PLoS ONE, vol. 11, no. 2 : e0146759. doi:10.1371/journal.pone.0146759, 2016.
    2. Jagannadham, J., Jaiswal, H.K., Rawal, K., Deciphering relationships in disease networks using computational approaches: Fatty Liver, PCOD, Osteoarthritis, cholelithiasis & hyperlipdemia", International Journal of PharmTech Research, vol. 8, no. 1, pp. 127-134, 2015.
    3. Jagannadham, J., Jaiswal, H.K., Agarwal, S., Rawal, K., Biomedical Text Mining of Obesity, Diabetes and hypertension genes. International Journal of Pharmaceutical Sciences Review and Research. vol 33(2), 182-186, 2015.
    4. Agrawal, S., Rawal, K.,Sahu, A., Mahajan, S., Garg, P. and Bahl, R.,"To find gene distributions in PubMed abstracts using Perl software", Journal of Pharmacy Research 2013.
    5. Jaiswal, H.K., Rawal, K.,Jaganadham, J., Agrawal, S., “Evaluation of inhibition activity of Tetrahydrolipstatin analogues on Diacylglycerol lipase alpha using In-silico techniques”. Journal of Pharmacy Research, vol.5, no.6, pp. 3473-3477, 2012.
  3. Built a new system for finding role of microRNAs in heart development and heart diseases by integration of large scale experimental data with computational and comparative approaches. We detected 353 known and 703 novel miRNAs involved in heart development. The target mRNAs were appeared to be enriched with genes related to cell cycle, apoptosis, signaling pathways, extracellular remodeling, metabolism, chromatin remodeling and transcriptional regulators
    1. Rustagi Y, Jaiswal HK, Rawal, K., Kundu GC, Rani V (2015). Comparative Characterization of Cardiac Development Specific microRNAs: Fetal Regulators for Future. PLoS ONE.10(10): e0139359. https://doi.org/10.1371/journal.pone.0139359
    2. Gupta, R., Soni, Patnaik, Sood, I., Singh, R., Rawal, K., Rani, V. High AU content: A Signature of Upregulated miRNA in Cardiac Diseases, Bioinformation, vol. 3, pp.132-135, 2010
  4. Vaccine Development
    1. Abbasi, B. A., Saraf, D., Sharma, T., Sinha, R., Singh, S., Gupta, P. Rawal, K. (2020, April 8). Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches. https://doi.org/10.31219/osf.io/f8zyw
    2. Kamal Rawal, v, Abbasi, B. A., et al (2020). Design of a multi-epitope Chagas disease vaccine by computational analysis of the Trypanosoma cruzi CL Brenner proteome. (Communicated)
    3. Rawal k, Sinha R et al (2020) Vaxi – DL : A web-based Deep Learning (DL) server to identify Potential Vaccine Candidates, (Bioinformatics- communicated)
  5. COVID-19
    1. Jethani B., Rawal, K. et al (2020). Clinical Characteristics and Remedy Profile of Patients with COVID-19: Retrospective Cohort Study, Accepted (In Press)
    2. Rawal, K., Sinha R et al (2020) To Study the Effect of Unconventional Treatment Protocol on COVID-19 patients in Delhi using AI based techniques (Lancet- communicated)
  6. Machine Learning
    1. Rawal, K., Khurana T, Sharma H, . 2019. An extensive survey of molecular docking tools and their applications using text mining and deep curation strategies. PeerJ Preprints7:e27538v1 https://doi.org/10.7287/peerj.preprints.27538v1
  7. Established the new framework/portal of "obesity treatment" and other diseases such as type 2 diabetes and hypertension among the people living sedentary lifestyle by engaging relevant sections of community using social networks, data science systems, machine learning, sensors and mobile apps. The system is being used by general public as a social service initiative.
    1. Development of a Web Based Weight Loss Programme. K. Rawal, P. Gaur and K. Kashive, LAP LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken Germany, October, 2014
    2. Developing Networks in Obesity using Text Mining. K. Rawal, S. Agarwal and J. Jagannadham, LAP LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken Germany, September, 2014.