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Integrating Multiple Data Sources to Power Discovery and Analysis

Precision Medicine World Conference (PMWC) took place in January in Mountain View, California, and offered attendees the opportunity to learn about innovative technologies, initiatives, and clinical case studies that are catalyzing the adoption of precision medicine in the clinic. DNAnexus was pleased to host a panel to discuss scalable infrastructure/platforms integrating next-generation sequencing (NGS) and other data (e.g. phenotypic) to power discovery and analysis in pharma and the clinic. Learn more below and watch the panel discussion.

Moderator: DNAnexus, Brady Davis, Chief Strategy Officer

 

Panelists:
AstraZeneca/MedImmune – David Fenstermacher, VP BioInformatics

Sutter Health – Greg Tranah,  Director, CPMC Research Institute, Adjunct Professor Dept. of Epidemiology & Biostatistics, UCSF

 Carol Franc Buck Breast Cancer Center at UCSF– Laura Esserman, Director

City of Hope – Sorena Nadaf, SVP & CIO

 

Abstract:

Health care providers increasingly require multi-omic datasets, including phenotypic data informed by genomic data. Such data needs to be obtained in an economically sustainable way and made available on an agile user-friendly platform so that these data may inform clinical care and lead to health improvements.

Pharmaceutical companies are interested in obtaining datasets containing phenotypic/clinical and genomic information generated from patient cohorts of specific disease areas. Such datasets can help pharma researchers identify drug targets or find biomarkers, validate hypotheses related to the interaction of genomics with disease or with specific therapies, and identify candidate populations for future clinical trials. Payers are also interested in the outcomes related to new discoveries and therapies in order to reimburse for these treatments.

This discussion focuses on how healthcare provider organizations, pharmas and payers are working toward solving these complex and challenging problems from a technical and business model perspective.

 

 

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