Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer.
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Network biomarkers reveal dysfunctional gene regulations during disease progressionPredicting disease progression from short biomarker series using expert advice algorithmIntermittent Androgen Suppression: Estimating Parameters for Individual Patients Based on Initial PSA Data in Response to Androgen Deprivation Therapy.Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers.Identifying critical transitions and their leading biomolecular networks in complex diseases.Mathematical modelling of prostate cancer growth and its application to hormone therapy.Mathematically modelling and controlling prostate cancer under intermittent hormone therapyDefining and characterizing the critical transition state prior to the type 2 diabetes disease.Quantitative mathematical modeling of PSA dynamics of prostate cancer patients treated with intermittent androgen suppression.Cancer dynamics for identical twin brothers.Nonlinear systems identification by combining regression with bootstrap resampling.A stochastic model of cancer growth subject to an intermittent treatment with combined effects: reduction in tumor size and rise in growth rate.A partial differential equation model and its reduction to an ordinary differential equation model for prostate tumor growth under intermittent hormone therapy.Personalizing Androgen Suppression for Prostate Cancer Using Mathematical Modeling.Parameter estimation and optimal scheduling algorithm for a mathematical model of intermittent androgen suppression therapy for prostate cancer.Describing high-dimensional dynamics with low-dimensional piecewise affine models: applications to renewable energy.Piecewise affine systems modelling for optimizing hormone therapy of prostate cancer.Theory of hybrid dynamical systems and its applications to biological and medical systems.Optimal Finite Cancer Treatment Duration by Using Mixed Vaccine Therapy and Chemotherapy: State Dependent Riccati Equation Control
P2860
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P2860
Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
2010年學術文章
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2010年學術文章
@zh-hant
name
Development of a mathematical ...... e therapy for prostate cancer.
@en
Development of a mathematical ...... e therapy for prostate cancer.
@nl
type
label
Development of a mathematical ...... e therapy for prostate cancer.
@en
Development of a mathematical ...... e therapy for prostate cancer.
@nl
prefLabel
Development of a mathematical ...... e therapy for prostate cancer.
@en
Development of a mathematical ...... e therapy for prostate cancer.
@nl
P50
P1476
Development of a mathematical ...... e therapy for prostate cancer.
@en
P304
P356
10.1016/J.JTBI.2010.02.027
P407
P577
2010-02-20T00:00:00Z