Big Data in Life Sciences

November 13 | 4:30 p.m. - 7 p.m. (Registration at 4 p.m.)

Presented by NC COIN [2] and NCBiotech

Big data in life sciences is characterized by qualitative changes in scale and complexity fueled by exponentially falling costs of generating, storing, analyzing and sharing massive biomedical data sets that include molecular, imaging, clinical, environmental and lifestyle data. Key enabling technologies include next generation sequencing, genetic engineering, machine learning, internet of things, cloud computing and data virtualization. Big data platforms provide unprecedented evidence for correlations between characteristics of individuals on the one hand and disease prevention, disease diagnoses, prognoses, and prediction of response to pharmacological, surgical, or behavioral treatments on the other hand.

Contemporaneous with this quantum leap in technological capabilities was the broad recognition that the gap between healthcare expenditures and outcomes had reached an unsustainable level – in no small part due to unfocused trial and error use of precious biomedical resources from both an R&D and clinical perspective. Precision health is the term applied to a big data driven system to get the right treatment to the right patient in the right dose in the right place at the right time – thus reducing waste from treatments that had no chance of working in the first place. Although precision health has been largely driven by more precisely identifying genomic mutations, it has been applied to other therapeutic areas, public health policy models, and value-based pricing models for reimbursements as well.

Despite examples of successful use of big data to identify biomarkers such as PD-L1, there remain nontrivial obstacles to realizing the potential of precision health in affecting patient outcomes (e.g., progression free survival) or accelerating drug discovery. These issues include: lack of harmonization among biomedical datasets; disincentives toward data sharing; false positive findings from multiple hypothesis testing problems; untangling multiple genetic, epigenetic, transcriptional, and environmental factors in disease etiology and treatment; reconciling more precise patient stratification with exigencies of patient enrollment timelines in clinical trials; lack of interpretability/actionability by clinicians; and reimbursement issues for molecular diagnostic testing.

The panel will explore these issues from the unifying perspective of improving patient outcomes and will explain how they (and others) are specifically addressing these issues. They will also discuss "white space" for areas of innovation that have the potential for disruptive impact.

Moderator:

Richard E. Kouri, Ph.D., MS - Chief Evangelist, Center for Innovation Management Studies; Adjunct Professor, College of Agriculture and Life Sciences (Biotechnology and Genetics), and College of Engineering (Biomedical Engineering), North Carolina State University

Panelist:

Russell D. Wolfinger, PhD - Director of Scientific Discovery and Genomics, SAS Institute Inc.

William A. Glauser, Ph.D., MBA - Partner, IOI Partners

Dimitris Agrafiotis, PhD, FRSC - Chief Data Officer, Covance

Michael Stocum, MS - Chief Executive Officer, Inivata

Lead Sponsors:

   

 

 

Agenda: 

4 p.m. - Registration 

4:30 p.m. - Program

6 p.m. - Networking Reception

Registration: 
 

$25 online (register by November 9)
$35 onsite (credit cards only)