Leveraging big data using a novel clinical database and analytic platform based on 323,145 individuals with and without of Diabetes ::
Hiranjith G.H., Anjana Ranjit Mohan, Praveen Raj, Jebarani Saravanan, Srinivasan Vedantham, Radha Venkatesan, Muthu Narayanan, Pradeepa Rajendra, Ranjit Unnikrishnan, Somasekhar Jayaram, Rohit Gupta, Paul George, Brijendra Kumar Srivastava, Uthra Subash Chandra Bose, Lovelena Munawar, Sam Santhosh, Mohan Viswanathan
Diabetes is a chronic disorder of glucose metabolism and is a major cause of heart disease and end-stage renal disease in world populations. It is also the single biggest cause of preventable blindness, the leading cause of non-traumatic lower extremity amputation and major cause of premature mortality. 415 million people have diabetes globally and is expected to reach 642 million by 20401. Large volumes of diabetes biomedical data are being produced every day, but it has not been used effectively. Leveraging such voluminous amount of patient data using data science approaches help to uncover hidden patterns, unknown correlations, and other insights of the disease. Integration of diverse genomic data with comprehensive electronic health records (EHRs) exhibit challenges, but essentially, they provide a feasible opportunity to better understand the underlying diseases, treatment patterns and develop an efficient and effective approach to identify biomarkers for diagnosis and improve therapy.
The value of studying the Indian population to identify novel genetic variants to inform mechanisms of disease and pharmacological response ::
A. Das, P. Raj, V. Gopalan, Hiranjith. G.H., E. Stawiski, S. Santhosh, R. Gupta, A. Chaudhuri, R. Gupta
While Genome wide association studies can shed light on the significance of variants in susceptibility to a disease or allow to stratify patients for specific therapeutic modalities, often variants that are rare and could be of significance are not identified in these studies. This can occur due to allelic heterogeneity in a complex disease. Furthermore, spurious differences in allelic frequencies between normal and disease resulting from systematic differences in ancestry can also confound the conclusions drawn from a GWAS study. Therefore, studying population isolates where individuals with the disease and normal have a homogeneous genetic background can allow to enrich for rare alleles, and improve the accuracy of elimination of false positives, and make it possible to accurately correlate segregation of the variants to the disease traits. One such population is of the Indian subcontinent, where the ancestral populations date back to modern humans travelling out of Africa 65,000 year ago, creating a gene pool of over 1000 years starting from a few founder families, resulting in an accumulation of unique disease-causing and disease-protective alleles that were preserved and enriched within various ethnic groups in the country.
OncoPeptVAC : A machine learning based approach for candidate vaccine identification and their validation using cell based assays ::
Ankita Das, Priyanka Shah, Xiaoshan “Shirley” Shi, Vasumathi Kode, Kayla Lee, Ravi Gupta, Amit Chaudhuri and Papia Chakraborty
The Ophthatome™ Knowledgebase : A curated knowledgebase of over 500,000 ocular disease phenotypic records coupled with analyses tools to enable novel discoveries for drug development and pharmacogenomics ::
A. Das, Nagasamy S, P. Raj, B. Muthu Narayanan, J. Somasekhar, T. Chandrasekhar, D. Kumar, A. Shetty, S. Das, S. Tejwani, P. Narendra, A. Ghosh