Abstract

Public Health Relevance

The massive generation of molecular information in oncology will not in itself change cancer death rates. This highlights the need to transition from archiving and binning facts to building predictive models of disease that help patients. Probabilistic causal models with curated data will allow early detection markers and directed therapies as well as predicting outcomes. The Sage CCSB will enable this distributed model building, while training scientists, building interface tools, and linking models between sites.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA149237-04
Application #
8567621
Study Section
Special Emphasis Panel (ZCA1-SRLB-C)
Project Start
2013-03-18
Project End
2014-02-28
Budget Start
2013-03-18
Budget End
2014-02-28
Support Year
4
Fiscal Year
2013
Total Cost
$2,124,509
Indirect Cost
$798,935

  Institution

Name
Sage Bionetworks
Department
Type
DUNS #
830977117
City
Seattle
State
WA
Country
United States
Zip Code
98109

  Publications

Nikolova, Olga; Moser, Russell; Kemp, Christopher et al. (2017) Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies. Bioinformatics 33:1362-1369
Mikheev, Andrei M; Mikheeva, Svetlana A; Trister, Andrew D et al. (2015) Periostin is a novel therapeutic target that predicts and regulates glioma malignancy. Neuro Oncol 17:372-82
Dienstmann, Rodrigo; Jang, In Sock; Bot, Brian et al. (2015) Database of genomic biomarkers for cancer drugs and clinical targetability in solid tumors. Cancer Discov 5:118-23
Commo, F; Ferté, C; Soria, J C et al. (2015) Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology. Ann Oncol 26:582-8
Chaibub Neto, Elias (2015) Speeding Up Non-Parametric Bootstrap Computations for Statistics Based on Sample Moments in Small/Moderate Sample Size Applications. PLoS One 10:e0131333
Guinney, Justin; Dienstmann, Rodrigo; Wang, Xin et al. (2015) The consensus molecular subtypes of colorectal cancer. Nat Med 21:1350-6
Jang, In Sock; Dienstmann, Rodrigo; Margolin, Adam A et al. (2015) Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors. Pac Symp Biocomput :32-43
Gönen, Mehmet; Kaski, Samuel (2014) Kernelized Bayesian Matrix Factorization. IEEE Trans Pattern Anal Mach Intell 36:2047-60
Gönen, Mehmet (2014) Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning. Pattern Recognit Lett 38:132-141
Gönen, Mehmet; Margolin, Adam A (2014) Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning. Bioinformatics 30:i556-63

Showing the most recent 10 out of 30 publications