It has become feasible to generate deep quantitative data for many of the molecules that are functional in cells, making it possible to survey a large number of tumors measuring genomic alterations and changes to transcripts, proteins and metabolites. It is, however, not clear what is the best way to integrate these data sets to extract as much information as possible about the biology that drives the cancer and how to best disrupt the tumor growth. Our proposed Proteogenomic Data Analysis Center for Cancer Systems Biology and Clinical Translation will develop new methods for better analyzing and integrating these data sets. In addition to developing statistical and machine learning methods, we also emphasize visual exploration of the data, and we will implement interactive web browser based visualization that will allow researchers to easily explore these vast data sets and gain novel insights by being able to quickly switch between summary information and details of the raw data.
The mission of the proposed data analysis center is to leverage high dimensional large-scale data from tumor samples to identify new avenues for the development of clinical prognostics and therapeutics for cancer. This mission will be realized through analysis, integration and visualization of multi-omic datasets including genomic, transcriptomic, and proteomic data collected from patient samples to develop predictive models, and during drug treatment of patient derived xenografts and cell lines to validate mechanisms.
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