F
Fang Jin
Researcher at University of California, Irvine
Publications - 6
Citations - 922
Fang Jin is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Tumor progression & Parametric statistics. The author has an hindex of 4, co-authored 6 publications receiving 842 citations. Previous affiliations of Fang Jin include University of Texas Health Science Center at Houston & University of California.
Papers
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Journal ArticleDOI
Nonlinear modelling of cancer: Bridging the gap between cells and tumours
John Lowengrub,Hermann B. Frieboes,Hermann B. Frieboes,Fang Jin,Fang Jin,Yao-Li Chuang,Xiangrong Li,Paul Macklin,Steven M. Wise,Vittorio Cristini +9 more
TL;DR: In this paper, the authors provide an overview of multiscale modelling focusing on the growth phase of tumours and bypassing the initial stage of tumourigenesis, and limit the scope further by considering models of tumor progression that do not distinguish tumour cells by their age and do not consider immune system interactions nor do they describe models of therapy.
Journal ArticleDOI
Three-Dimensional Multispecies Nonlinear Tumor Growth–II: Tumor Invasion and Angiogenesis
Hermann B. Frieboes,Fang Jin,Fang Jin,Yao-Li Chuang,Steven M. Wise,John Lowengrub,Vittorio Cristini +6 more
TL;DR: Heterogeneity in the physiologically complex tumor microenvironment, caused by non-uniform distribution of oxygen, cell nutrients, and metabolites, as well as phenotypic changes affecting cellular-scale parameters, can be quantitatively linked to the tumor macro-scale as a mechanism that promotes morphological instability.
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Multiparameter Computational Modeling of Tumor Invasion
Elaine L. Bearer,John Lowengrub,Hermann B. Frieboes,Yao-Li Chuang,Fang Jin,Steven M. Wise,Mauro Ferrari,David B. Agus,Vittorio Cristini +8 more
TL;DR: This work establishes a framework for monitoring system perturbation towards development of therapeutic strategies and correlation to clinical outcome for prognosis by quantifying the link between the tumor boundary morphology and the invasive phenotype.
Journal ArticleDOI
Predicting simulation parameters of biological systems using a Gaussian process model
TL;DR: A novel approach based on a Gaussian process model that addresses the two issues jointly of parametric regression and stochastic nature of most biological simulations, and the existence of a potentially large number of other factors that affect the simulation outputs is proposed.