Medidata AI Omics Webcast
Transforming Clinical Development and Discovery with Multi-omic Data in the Era of Precision Medicine
Technological and computing advances have enabled the generation of vast amounts of translational data for the discovery of biomarker signatures of disease and drug response. However, leveraging these data within clinical trials remains a challenge. Data Management teams need to contend with new data types and multiple data vendors providing data without standardized formats. Bioinformatics and data science teams are often faced with time-consuming processes requiring that they spend the majority of their time gathering, cleaning and validating data to create the analysis dataset, leaving little time for the in-depth analysis that can lead to valuable insights.
In this on-demand webinar, we will demystify the inclusion of omics data in your clinical trials. Industry experts will discuss key challenges, propose best practices and share success stories illustrating how incorporating translational omics data into clinical development yielded novel insights and improved patient/study outcomes.
Who Should View:
- Data Management teams
- Scientific teams: Translational Medicine, Precision Medicine, Biomarker Discovery
|Ana Oromendia, PhD
Director of Product, Value Discovery
Medidata AI, A Medidata Company
Ana Oromendia is the Product Director for Value Discovery Solutions at Medidata AI. She leads the development of scientific data and analytics tools to improve the efficiency and rigor in the therapeutic development process with the ultimate goal of getting more efficacious treatments to patients faster. She has spent her career at the intersection of genomics and technology, developing a genetic diagnostic test at a women’s health biotech, and investigating the proteomic effects in solid tumors and neurodegenerative disease. Ana earned a PhD in molecular genetics from MIT and a BS in biochemistry from the University of Minnesota.
|Jason Mezey, PhD
Consultant & Lead Architect of Genomics Products
Jason Mezey currently holds the rank of Full Professor with lifetime tenure at Cornell University in the Department of Computational Biology. He is also concurrently a Full Professor in the Department of Genetic Medicine at Weill Cornell Medicine in New York City. Dr. Mezey has spent the last two decades working in the field of genomics where he develops cutting-edge computational statistics and machine learning methods for data analysis. He is currently collaborating with Medidata, where he develops analytics methodology to support the use of genomic data in clinical trials. Dr. Mezey earned a BA from the University of Pennsylvania and a PhD from Yale University, where his work focused on statistical genetics.