Title: Data Management and Analytics for Research into Rare Diseases: Presenting The Phelan McDermid Syndrome Data Network
Speaker: Cartik Saravanamuthu, Research Fellow, Harvard Medical School
Venue: CC-101 (in the new CC building next to KRESIT)
Date and Time: 19th January,11:30am - 12:30pm
Abstract:
Research into the origin and progression of rare diseases is hindered by small patient populations and the consequent lack of patient data. Phelan-McDermid Syndrome (PMS) is a rare genetic disorder with less than 1500 diagnosed cases worldwide. Given the scarcity of patient data, the Phelan-McDermid Syndrome Data Network (PMS_DN), a research initiative with the objective of uncovering associations between genetic variants and clinical symptoms of PMS, leverages clinical notes and self-reported outcomes of PMS patients for extracting knowledge about PMS. This talk will discuss the data integration and analytics architecture that underlies the PMS_DN project and a few insights gleaned from analysis of the integrated datasets on PMS_DN. In addition, broader precision medicine research initiatives at the Harvard Medical School Department of Biomedical Informatics will be discussed as well.
Brief Bio:
Cartik Saravanamuthu holds a Ph.D. in computer engineering from the University of Memphis. His postdoctoral work has included stints in academic research as well as consulting and leadership positions with industry startups in India and in Silicon Valley, California. Saravanamuthu’s research work has been primarily in the use of Semantic Web ontologies for a) annotation and extraction of knowledge from unstructured text, b) the integration of complex, heterogeneous data from multiple sources, and c) the use of intelligent reasoning strategies to infer high-level knowledge from the integrated data. In the area of biomedical informatics, his research projects have used ontology-based data integration and reasoning to uncover novel associations between genotypic variance and phenotypic diversity. At DBMI, he uses machine learning techniques on clinical data of patients diagnosed with Phelan McDermid Syndrome, a rare syndromic variant of Autism Spectrum Disorder. Saravanamuthu works with patient self-reported outcomes data and clinical notes from electronic health records to identify patient subtypes based on phenotype profiles, refine ontological definitions of patient symptoms and conditions, and advance the knowledge about Phelan McDermid Syndrome. It can also be found online at https://dbmi.hms.harvard.edu/people/cartik-saravanamuthu