A major research focus for gaining insight into the molecular basis for cancer, with the goal of identifying new treatments, is the systematic profiling of cancer cells. This includes the generation of compound potency data and gene/protein expression data. This has led to the generation of massive amounts of disparate data that must be associated in context sensitive ways and collectively data mined in order to reach the goal of finding new treatments.
The aim of this project is to develop a new computing platform that will enable researchers throughout the world to interactively collaborate on the analysis of this valuable information. A new web-based platform will be developed that, incorporates recent and emerging Web 2.0 and Enterprise software standards and technologies. This platform will incorporate public cancer cell-based data, including genomics, proteomics, and cytotoxic potency data. It will make possible the systematic exploration of the data through interactive data analysis and visualization tools in order to generate hypotheses. The proposed project will include the development of new statistical methods to identify groups of cells over which selected genes and compounds are highly and significantly correlated. The platform will be used to identify candidate target genes and drugs for cancer chemotherapy, and to determine factors that contribute to chemoresistance and sensitivity.
Cancer is a major cause of mortality throughout the world. Many types of cancers lack effective treatments, and new treatment approaches have the potential to positively impact large patient populations. This project will build a new data analysis and visualization platform to aid in identifying biomarkers predictive of drug efficacy for optimizing cancer chemotherapy.
