Vandana
Assistant Professor
Vandana is an Assistant Professor at the Faculty of Applied Sciences and Biotechnology at Shoolini University. She completed her PhD from Shoolini University and also holds an MPhil in Biotechnology from the same institution. She earned her Master’s degree in Bioinformatics from Panjab University and completed her Bachelor’s degree in Bioinformatics from Dolphin College of Life Sciences, Patiala University.
Her research focuses on computational biology, particularly Next-Generation Sequencing (NGS) data analysis using RNA sequencing pipelines. She has published research articles in reputed SCI- and Scopus-indexed journals and has contributed book chapters in recognised academic publications. She has also presented her research at several national and international conferences.
With over two years of professional experience, she teaches subjects such as Bioinformatics Applications, Data Structures and Algorithms, Computational Biology, and Food Biotechnology. She also serves as a Course Coordinator, managing various academic and administrative responsibilities within the department.
Vandana is passionate about teaching and is known for creating an engaging and supportive learning environment that encourages student participation and curiosity. Through her academic and administrative roles, she actively contributes to strengthening teaching, research, and student learning within the department.
Publications
- Guleria, V., & Jaiswal, V. (2020). Comparative transcriptome analysis of different stages of Plasmodium falciparum to explore vaccine and drug candidates. Genomics, 112(1), 796-804.
- Vandana, & Jaiswal, V. (2020). Molecular docking studies to design potential antimalarial compounds targeting RAB-6.
- Vandana, Khan, S., Gupta, G., & Mahajan, S. (2023, November). Deep Learning Applications for Malaria Detection and Diagnosis: A Review. In International Conference on Artificial Intelligence and its Application (pp. 49-65). Cham: Springer Nature Switzerland.
- Guleria, V., Pal, T., Sharma, B., Chauhan, S., & Jaiswal, V. (2021). Pharmacokinetic and molecular docking studies to design antimalarial compounds targeting Actin I. International journal of health sciences, 15(6),
- Gupta, G., Khan, S., Guleria, V., Almjally, A., Alabduallah, B. I., Siddiqui, T., ... & Al-Subaie, M. (2023). DDPM: A dengue disease prediction and diagnosis model using sentiment analysis and machine learning algorithms. Diagnostics, 13(6), 1093.
- Almarri, B., Gupta, G., Kumar, R., Vandana, V., Asiri, F., & Khan, S. B. (2024). The BCPM method: decoding breast cancer with machine learning. BMC medical imaging, 24(1), 248.
- Kumar, H., Guleria, S., Kimta, N., Dhalaria, R., Guleria, V., Cimler, R., & Kuca, K. (2024). Nanoparticles-Mediated Diagnosis of Common Human Diseases: With Special Reference to Gold Nanoparticles. In Nanotechnology: Agriculture, Environment and Health (pp. 227-243). Singapore: Springer Nature Singapore.
- Kimta, N., Dhalaria, R., Kuča, K., Cimler, R., Guleria, V., Guleria, S., & Kumar, H. (2024). Production of metallic nanoparticles from agriculture waste and their applications. In Transforming Agriculture Residues for Sustainable Development: From Waste to Wealth (pp. 131-156). Cham: Springer Nature Switzerland.
- Siddiqui, S. A., Hadus, M. C. I., Fitriani, A., Guleria, V., Kuppusamy, S., Bhattacharjee, B., ... & Maggiolino, A. (2024). Edible cockroaches as food and feed–A systematic review on health benefits, nutritional aspects and consumer acceptance. Journal of Insects as Food and Feed, 12(2), 179-221.
- Naithani, U., & Guleria, V. (2024). Integrative computational approaches for discovery and evaluation of lead compound for drug design. Frontiers in Drug Discovery, 4, 1362456.
Book Chapters:
- Naithani, U., Guleria, T., & Vandana. (2025). Analysis of drug-likeness and toxicity prediction of lead compounds for veterinary applications. In Bioinformatics in Veterinary Science: Vetinformatics (pp. 307-323). Singapore: Springer Nature Singapore.
- Choudhary, S., Guleria, V., & Nagraik, R. Proteomics techniques for characterizing microbial proteins.
- Verma, D., Guleria, V., & Sharma, A. Enhancing obstructive sleep apnea diagnosis with machine learning: innovations and outcomes.
- Verma, D., Guleria, V., & Sharma, A. Enhancing obstructive sleep apnea diagnosis with machine learning: innovations and outcomes.
Email id: vandana@shooliniuniversity.com
LinkedIn profile: https://www.linkedin.com/in/vandanaguleria/
Google Scholar profile: https://scholar.google.com/citations?user=QQPI6y8AAAAJ&hl=en
Scopus ID: https://www.scopus.com/authid/detail.uri?authorId=57209301104