Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics
A stochastic model is introduced to describe the growth of cancer affected by anti-cancer therapeutics of Chondroitin Sulfate (CS). The parameters values of the stochastic model are estimated via maximum likelihood function. The numerical method of Euler-Maruyama will be employed to solve the model...
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ump-186482018-01-24T04:05:47Z http://umpir.ump.edu.my/id/eprint/18648/ Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics Mazma Syahidatul Ayuni, Mazlan Norhayati, Rosli Nina Suhaity, Azmi Solachuddin Jauhari, Arief Ichwan QA Mathematics A stochastic model is introduced to describe the growth of cancer affected by anti-cancer therapeutics of Chondroitin Sulfate (CS). The parameters values of the stochastic model are estimated via maximum likelihood function. The numerical method of Euler-Maruyama will be employed to solve the model numerically. The efficiency of the stochastic model is measured by comparing the simulated result with the experimental data. IOP Publishing 2017-09 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/18648/8/Ayuni_Mazlan_2017_J._Phys.%253A_Conf._Ser._890_012085.pdf Mazma Syahidatul Ayuni, Mazlan and Norhayati, Rosli and Nina Suhaity, Azmi and Solachuddin Jauhari, Arief Ichwan (2017) Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics. Journal of Physics: Conference series, 890 (012085). pp. 1-6. ISSN 1742-6588 (print); 1742-6596 (online) http://dx.doi.org/10.1088/1742-6596/890/1/012085 DOI:10.1088/1742-6596/890/1/012085 |
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QA Mathematics Mazma Syahidatul Ayuni, Mazlan Norhayati, Rosli Nina Suhaity, Azmi Solachuddin Jauhari, Arief Ichwan Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics |
description |
A stochastic model is introduced to describe the growth of cancer affected by anti-cancer therapeutics of Chondroitin Sulfate (CS). The parameters values of the stochastic model are estimated via maximum likelihood function. The numerical method of Euler-Maruyama will be employed to solve the model numerically. The efficiency of the stochastic model is measured by comparing the simulated result with the experimental data. |
format |
Article |
author |
Mazma Syahidatul Ayuni, Mazlan Norhayati, Rosli Nina Suhaity, Azmi Solachuddin Jauhari, Arief Ichwan |
author_facet |
Mazma Syahidatul Ayuni, Mazlan Norhayati, Rosli Nina Suhaity, Azmi Solachuddin Jauhari, Arief Ichwan |
author_sort |
Mazma Syahidatul Ayuni, Mazlan |
title |
Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics |
title_short |
Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics |
title_full |
Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics |
title_fullStr |
Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics |
title_full_unstemmed |
Modelling the Cancer Growth Process by Stochastic Differential Equations with the Effect of Chondroitin Sulfate (CS) as Anticancer Therapeutics |
title_sort |
modelling the cancer growth process by stochastic differential equations with the effect of chondroitin sulfate (cs) as anticancer therapeutics |
publisher |
IOP Publishing |
publishDate |
2017 |
url |
http://umpir.ump.edu.my/id/eprint/18648/ http://umpir.ump.edu.my/id/eprint/18648/ http://umpir.ump.edu.my/id/eprint/18648/ http://umpir.ump.edu.my/id/eprint/18648/8/Ayuni_Mazlan_2017_J._Phys.%253A_Conf._Ser._890_012085.pdf |
first_indexed |
2023-09-18T22:26:32Z |
last_indexed |
2023-09-18T22:26:32Z |
_version_ |
1777416003463938048 |