The Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG) at Mount Sinai will assemble, organize and visualize data collected from the under-studied druggable genome from the four families: protein kinases, nuclear receptor, ion channels and GPCRs. The KMC-IDG will also attempt linking such under-studied druggable targets for their potential applications in various diseases. To achieve this we will assemble and abstract data from four domains: proteins/genes/targets, drugs/perturbagens, diseases/phenotypes/side-effects, and data from individual patients. Various pipe-lines and workflow will be established to connect clusters of patients from various diseases to under-studied druggable targets. The Ma'ayan and Dudley Labs are well positioned to carry out successfully this project based on their prior track record of productivity, foundation of source code and data that is already collected and organized, and strong existing user base that can be directed to the newly developed portal. In addition, both labs have a strong track record of collaborations including the computational identification and experimental validation of at least one under-studied protein kinase as a potential important target for attenuating kidney fibrosis. One unique and innovative research component of this project is an investigation into the sources of the literature and experimental biases that exist in the molecular and cellular biology research domains.

Agency
National Institute of Health (NIH)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54CA189201-01
Application #
8785466
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Zenklusen, Jean C
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Icahn School of Medicine at Mount Sinai
Department
Pharmacology
Type
Schools of Medicine
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10029
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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