Parag Mallick, PhD,
Associate Professor, Department of Radiology
Canary Center at Stanford, Stanford Medicine
Thursday, March 7th, 2019, 12:00 pm to 1:00 pm
MSOB Conference Room X-275
Initiatives like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been launched in the past decade to examine the multi-omic relationships that drive cancer behavior. The analyses of multi-omics data have highlighted interesting clinical subtypes. It has also revealed the incredibly complex relationships that exist between scales. Unfortunately, the analyses of multi- omics data are exponentially more challenging that of single-ome analyses. Seemingly subtle changes in workflow can have dramatic impacts on findings. Building on top of an intelligent semantic workflow system, we captured the analytic methods of key proteogenomic papers as workflows and executed them systematically against diverse large multi-omics datasets. These studies revealed the fragility of multi-omic analyses. At the lowest levels (peptides identified), even trivially small changes had massive implications in what peptides or proteins were identified. Interestingly, higher-order findings such as patient strata were invariant to many perturbations. Ultimately, these studies suggest that even computational analyses, which we think of as highly systematic and reproducible, may be subject to many of the same issues as experimental studies.
Dr. Parag Mallick is an Associate Professor at Stanford University. Originally trained as an engineer and biochemist, his research spans computational and experimental systems biology, cancer biology and nanotechnology. Dr. Mallick received his undergraduate degree in Computer Science from Washington University in St. Louis. He then obtained his Ph.D. from UCLA in Chemistry & Biochemistry, where he worked with Dr. David Eisenberg. He completed Post- Doctoral studies at The Institute for Systems Biology, in Seattle, WA with Dr. Ruedi Aebersold. Beyond studying fundamental disease mechanisms, his group has been pioneering novel approaches for enabling personalized and predictive medicine.