Targeted MDM2 Degradation Reveals a New Vulnerability for p53-Inactivated Triple-Negative Breast Cancer is a research paper published in Cancer Discovery (2023). On theSindex it has a DataRank of 0. It has been cited 110 times.
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National Cancer Institute
Grant: CA272645
National Cancer Institute
Grant: CA181204
U.S. Department of Defense
Grant: W81XWH-19-1-0212
Ovarian Cancer Research Alliance
Grant: 813487
American Association for Cancer Research
Grant: 17-80-44-EISC
NCI NIH HHS
Grant: R01 CA181204
NCI NIH HHS
Grant: P30 CA056036
NCI NIH HHS
Grant: T32 CA009171
NCI NIH HHS
Grant: R01 CA272645
NIH HHS
Grant: S10 OD030245
NCI NIH HHS
Grant: P30 CA010815
National Institutes of Health
Grant: 2T32CA009171-21
TRAINING PROGRAM IN BASIC CANCER RESEARCH
National Institutes of Health
Grant: 1S10OD030245-01
Purchase of an Echo 650 acoustic liquid handler with Access workstation
National Institutes of Health
Grant: 3P30CA056036-22S1
Strengthening Community Engagement in Colorectal Cancer Screening and Clinical Trial Enrollment Through a Community Health Educator
National Institutes of Health
Grant: 1R01CA272645-01
Design of the First Mdm2 Targeting PROTACs for treatment of p53 Mutant or Deficient Cancers
National Institutes of Health
Grant: 5R01CA181204-02
Novel oncogenic functions of Mdm2 and Mdmx
National Institutes of Health
Grant: 5P30CA010815-28
CONSOLIDATED BASIC CANCER RESEARCH PROGRAM
FWCI
10.61
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data
Additional file 1 of A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data
Additional file 2 of A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data
Additional file 2 of A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data