Predicting and characterizing a cancer dependency map of tumors with deep learning is a research paper published in Science Advances (2021). On theSindex it has a DataRank of 0. It has been cited 89 times.
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National Institutes of Health
Grant: K99CA248944
National Institutes of Health
Grant: CTSA 1UL1RR025767-01
National Institutes of Health
Grant: R01GM113245
National Institutes of Health
Grant: P30CA54174
National Institutes of Health
Grant: T32GM113896
National Institutes of Health
Grant: F30AG057213
Cancer Prevention and Research Institute of Texas
Grant: RP160732
Fund for Innovation in Cancer Informatics
Grant: ICI Fund
Cancer Prevention and Research Institute of Texas
Grant: RR170055
Cancer Prevention and Research Institute of Texas
Grant: RP190346
NCI NIH HHS
Grant: P30 CA054174
NCRR NIH HHS
Grant: R01 RR170055
NCRR NIH HHS
Grant: UL1 RR025767
National Institutes of Health
Grant: 1K99CA248944-01
Deep learning of drug sensitivity and genetic dependency of pediatric cancer cells
National Institutes of Health
Grant: 1F30AG057213-01
Targeting Doxorubicin-Induced Mitochondrial Failure in Mesenchymal Stem Cells with Metformin
National Institutes of Health
Grant: 1R01GM113245-01
Collaborative Research:Graphical models for characterizing global RNA methylation
National Institutes of Health
Grant: 1UL1RR025767-01
CTSA INFRASTRUCTURE FOR CLINICAL TRIALS
National Institutes of Health
Grant: 3T32GM113896-03S1
South Texas Medical Scientist Training Program (STX-MSTP)
National Institutes of Health
Grant: 1S10RR013039-01
MICROPLATE SCINTILLATION/LUMINESCENCE/FLOUR COUNTER
National Institutes of Health
Grant: 5P30CA054174-16
SAN ANTONIO CANCER INSTITUTE
FWCI
16.56
Citation Percentile
1.0%
Citation Trend
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Sustainable Development Goals
Additional file 1 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 1 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 1 of TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Additional file 1 of TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Additional file 2 of TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Additional file 2 of TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Additional file 2 of A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Additional file 2 of A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Additional file 8 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 8 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 3 of TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Additional file 3 of TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Additional file 4 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 5 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 4 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 5 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 6 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 7 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 7 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Additional file 6 of Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes