Kalpana Raja

Kalpana Raja, PhD joined the Section of Biomedical Informatics & Data Science (BIDS) at Yale School of Medicine in February of 2023. Before moving to New Haven, CT, Kalpana worked as an assistant professor at the School of Biomedical Informatics, University of Texas Health Science Center (UTHealth) at Houston, TX. She also worked as a scientist and healthcare informatics at Sema4, a patient centered healthcare company located in Stamford, CT. Kalpana completed her bachelor’s degree in pharmacy from Tamil Nadu Dr. M.G.R. Medical University at Chennai, India. She is a registered pharmacist with the Indian Pharmacy Council. With a vision to develop software for biological applications, she completed her master’s degree in computing with a focus in software technology from The Robert Gordon University, Aberdeen, UK. She developed ProfileSKiM, an intelligent document retrieval tool, and submitted the findings in her MSc thesis. ProfileSKiM received a reward from the Robert Gordon University in 2005 and the Technology Award from the British Computer Society, London, UK in 2006. Kalpana completed her second master’s degree in bioinformatics and her PhD in computing: software technology – bioinformatics (inter-disciplinary) from Bharathiar University, Coimbatore, India. She presented her findings from the PhD research work at the 2012 Asia Pacific Bioinformatics Conference (APBC) held in Melbourne, Australia, and BioCreative Conference V held at Washington DC.

Kalpana’s research interests include natural language processing (NLP) and machine learning. She developed methodologies and software for information retrieval, information extraction, knowledge summarization, literature-based discovery, and automated hypothesis generation. She applied her approaches on various biological domains such as protein-protein interaction, protein phosphorylation, drug-drug interactions, adverse drug events, drug repurposing, and disease comorbidity. She also provided the NLP support for various genomics and transcriptomics projects. Kalpana has published more than 50 articles in peer reviewed journals, books, and conference proceedings. She has reviewed several research articles submitted to prestigious journals such as Briefings in Bioinformatics and serves as an associate editor in the Journal of Embryology & Stem Cell Research.

Kalpana was elected as a “Member of Royal Society of Biology” (MRSB) in 2019 by the Royal Society of Biology, London, UK. Recently, she was honored as the “Chartered Scientist” (CSci) by the Royal Society of Biology, London, UK. Kalpana also received the “2019 Women Scientist Award” from the Society for Bioinformatics and Biological Sciences, a non-profit professional society based in India.

Areas of Expertise

• Natural Language Processing
• Biomedical Text Mining
• Machine Learning
• Biomedical informatics
• Bioinformatics

Selected Professional Honors and Recognition

1. Chartered Scientist (CSci) from the Royal Society of Biology, London, UK (2022)
2. “2019 Women Scientist Award” from the Society for Bioinformatics and Biological Sciences, India (2020)
3. Chairperson at International Conference on Agriculture and Biological Sciences at Kathmandu (Lalitpur), Nepal (2020)
4. Curriculum Advisor for 2017 Regulation of Bachelor of Engineering in Pharmaceutical Engineering Program, Vinayaka Mission’s Kirupananda Variyar Engineering College, Salem, India (2019)
5. Member of Royal Society of Biology (MRSB) from the Royal Society of Biology, London, UK (2019)

Selected Publications

1. Prabahar, A. & Raja, K. Integrated approaches to identify miRNA biomarkers associated with cognitive dysfunction in multiple sclerosis using text mining, gene expression, pathways, and GWAS. Diagnostics (accepted).
2. Anand, D., Manoharan, S., Iyyappan, O.R., Anand, S., & Raja, K. Extracting significant comorbid diseases from MeSH index of PubMed. Springer Nature: Methods in Molecular Biology: Biomedical Text Mining. 2022, 283-299.
3. Patrick, M.T., Bardhi, R., Raja, K., He, K., & Tsoi, L.C. Advancement in predicting drug-drug interactions for drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources. Journal of the American Medical Informatics Association, 2021, ocaa335.
4. Prabahar, A., Shanmugam, L., Jose, M.A., Radhakrishnan, K.M., & Raja, K. A summary on up-to-date research on fungal siderophores on disease, treatment and pathogenicity based on text mining, bioinformatics and experts’ opinion. Springer Nature: Fungal siderophores – From Mineral-Microbe Interactions to Anti-pathogenecity. 2021, 187-210.
5. Raja, K., Natarajan, J., Kussisto, F., Steill, J., Ross, I., Thomson, J., & Stewart, R. Automated extraction and visualization of protein-protein interaction networks and beyond: a text mining protocol. Springer Nature: Methods in Molecular Biology, 2020, v.2074, 13-34.
6. Paul, D., Chipurupalli, S., Justin, A., Raja, K., & Mohankumar, S.K. Critical appraisal of Caenorhabditis elegans as a promising model to screen anti-Alzheimer’s therapeutics. Journal of Pharmacological and Toxicological Methods, 2020, 106, 106932.
7. Patrick, M.T., Raja, K., Miller, K., Sotzen, J., Gudjonsson, J.E., Elder J.T., & Tsoi, L.C. Drug repurposing prediction for immune-mediated cutaneous diseases using a word-embedding based machine learning approach. Journal of Investigative Dermatology, 2019, 139(3), 683-691.
8. Tsoi, L.C., Hile, G.A., Berthier, C.C., Sarkar, M.K., Reed, T.J., Uppala, R., Patrick, M.T., Raja, K., Xing, X., Xing, E., He, K., Gudjonsson, J.E., & Kahlenberg, J.M. Hypersensitive IFN Responses in Lupus Keratinocytes Reveal Key Mechanistic Determinants in Cutaneous Lupus. The Journal of Immunology, 2019, Article ID ji1800650.
9. Patrick, M.T., Stuart, P. E., Raja, K., Gudjonsson, J.E., Tejasvi, T., Yang, J., Chandran, V., Das, S., Callis-Duffin, K., Ellinghaus, E., Enerback, C., Esko, T., Franke, A., Kang, H.M., Krueger, G., Lim, H., Rahman, P., Rosen, C., Weidinger, S., Weichenthal, M., Wen, X., Voorhees, J.J., Abecasis, G., Gladman, D., Nair, R., Elder, J.T., & Tsoi, L.C. Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients. Nature Communications, 2018, 9(1), 4178.
10. Raja, K., & Natarajan, J. Mining protein phosphorylation information from biomedical literature using NLP parsing and Support Vector Machines (SVM). Computer Methods and Programs in Biomedicine, 2018, 160, 57-64.

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