The last few years have been an exciting time in the field of biomarker development. The approval of diagnostically driven drugs and patient selection refinement have improved outcomes for cancer patients. At the same time, advances in technology have allowed us to go from addressing only a few targeted questions in the context of a clinical study to large scale multi-omic profiling of disease.
These advances have allowed researchers to gain insight into drug response and disease biology that never would have been possible previously. The volume and complexity of the data as well as the rapid evolution of technology, however, have left many of today’s biomarker scientists without adequate training in the tools and statistical concepts needed to control and improve the sensitivity; specificity and reproducibility of multi-omic experiments. Fortunately, data analysis methods are also becoming increasingly refined and powerful as the data has become more mainstream.
In this seminar, Matt Wongchenko and Russ Wolfinger share recent success stories and practical guidance for improving everyday practice with omics data. These will include ways to:
- Leverage data for better scientific gain and a broadened data analysis mindset for optimizing the tradeoffs between false positives, false negatives and lack of reproducibility.
- Learn from how the new MAQC society, which has already achieved some progress in common examples such as lack of overlap in lists of differentially expressed genes and difficulty in validating biomarkers from genome-wide association studies.