Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
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Cancer stem cell metabolismEvaluating biomarkers to model cancer risk post cosmic ray exposureEarly mutation bursts in colorectal tumors.Notch-Jagged signalling can give rise to clusters of cells exhibiting a hybrid epithelial/mesenchymal phenotype.Deciphering Epithelial-Mesenchymal Transition Regulatory Networks in Cancer through Computational ApproachesOncofetal Epigenetic Bivalency in Breast Cancer Cells: H3K4 and H3K27 Tri-Methylation as a Biomarker for Phenotypic Plasticity.Predicting cell viability within tissue scaffolds under equiaxial strain: multi-scale finite element model of collagen-cardiomyocytes constructs.Extranuclear signaling by sex steroid receptors and clinical implications in breast cancer.Characterization of a Dual Rac/Cdc42 Inhibitor MBQ-167 in Metastatic Cancer.AKT2 siRNA delivery with amphiphilic-based polymeric micelles show efficacy against cancer stem cells.
P2860
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P2860
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
description
2015 nî lūn-bûn
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2015年の論文
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2015年論文
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2015年論文
@zh-hant
2015年論文
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2015年論文
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2015年論文
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2015年论文
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2015年论文
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2015年论文
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name
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
@ast
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
@en
type
label
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
@ast
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
@en
prefLabel
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
@ast
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
@en
P2093
P2860
P1433
P1476
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
@en
P2093
Alfonso Landeros
Kenneth Lange
Mary E Sehl
Max S Wicha
Miki Shimada
P2860
P304
P356
10.1371/JOURNAL.PONE.0135797
P407
P577
2015-09-23T00:00:00Z