The Data Organization Core (DOC) of the Mount Sinai's KMC-IDG will collect, process, and maintain attributes about the druggable targets for all proposed families: protein kinases, G-protein coupled receptors, nuclear receptors and ion channels. The emphasis will be to focus on those genes/proteins that are understudied and collect unbiased genome-wide profiling datasets. In addition, the DOC will collect, process and maintain data tables and attributes for all other genes/proteins, drugs/small-molecules and other perturbagens, pheontypes/diseases/side-effects, and clinical as well as genomics datasets from cohorts of patients. This will enable us to identify links between and across genes/proteins networks, drugs/small-molecules and other perturbagens networks, pheontypes/diseases/side-effects networks, and clusters of individual patients with similar profiles. For this, the Core will develop and apply clustering and classification algorithms as well as workflows to make predictions about the potential applicability of targeting the understudied proteins for various translational applications in personalized medicine.

Public Health Relevance

The large amount of data that is accumulating from genome-wide emerging biotechnologies is illuminating new biology about many genes that until recently not much data was available. This new knowledge, integrated with existing databases, can be used to prioritize potential genes/proteins as novel drug targets.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54CA189201-01
Application #
8934410
Study Section
Special Emphasis Panel (ZRG1-BST-M (50))
Program Officer
Zenklusen, Jean C
Project Start
2014-08-01
Project End
2016-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
1
Fiscal Year
2014
Total Cost
$449,160
Indirect Cost
$247,537
Name
Icahn School of Medicine at Mount Sinai
Department
Type
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029
Ma'ayan, Avi; Rouillard, Andrew D; Clark, Neil R et al. (2014) Lean Big Data integration in systems biology and systems pharmacology. Trends Pharmacol Sci 35:450-60
Ma'ayan, Avi; Duan, Qiaonan (2014) A blueprint of cell identity. Nat Biotechnol 32:1007-8
Duan, Qiaonan; Flynn, Corey; Niepel, Mario et al. (2014) LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res 42:W449-60
Duan, Qiaonan; Wang, Zichen; Fernandez, Nicolas F et al. (2014) Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets. Bioinformatics 30:3289-90