Our lab is interested in cataloging, analyzing and interpreting large-scale multidimensional data on tumors of head and neck region with a final goal to understanding how different variants may cause cancer and how we can use the molecular markers towards a better understanding of the disease. Additionally, we are interested in using molecular information of tumors towards prediction, personalized diagnosis, treatment and follow-up of head and neck cancer (HNSCC).
To begin with, we are focusing our work on oral cavity tumors (OCSCC), especially oral/anterior tongue tumors. Oral or anterior tongue squamous cell carcinoma (OTSCC) is an aggressive group of tumors, characterized by their early spread to lymph nodes and a higher rate of regional failure compared to other oral cavity tumors. Additionally, there is a rise in the incidence of OTSCC among younger population (<50yrs); many of who lack the typically associated risk factors (alcohol and tobacco exposure, both smoking and chewing) which makes this a significant group of cancer to study. We are also interested in understanding the role, if any, of human papillomavirus (HPV) in the development of OCSCC and survival in patients with OCSCC.
Bioinformatics and Computational Biology
We are interested and actively engaged in developing/optimizing analytical tools, pipelines, and methodologies for next-generation sequencing and microarray data, especially useful in the clinical environment. Additionally, we are interested in integrating multidimensional data/metadata to understand biological questions. We apply machine learning and deep learning tools and algorithms, optimize them to answer questions using genome-, transcriptome- and methylome-scale data towards assembly, annotation and understanding of gene functions.
We are studying genes involved in the production of the secondary metabolites in neem plant, Azadirachta indica, especially azadirachtin and its re-engineering. We use Artificial Intelligence-based tools to maximize the productivity by helping select the right plants possessing higher metabolite content, azadirachtin. Additionally, we use the plant's genome and transcriptomes' information to understand gene expression, annotation of the key terpenoid genes. Our ultimate goal is to produce plant-free secondary metabolites by metabolic engineering.