Finite-Set Model Predictive Control of Melanoma Cancer Treatment Using Signaling Pathway Inhibitor of Cancer Stem Cell

Javadi, Afsaneh; Keighobadi, Faezeh; Nekoukar, Vahab; Ebrahimi, Marzieh

doi:10.1109/tcbb.2019.2940658

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…Still, the support values don't produce effective feature selection because non-dimensionality leads to none related feature selection to reduce the classification accuracy. In [8], the Author described lung cancer prediction based on hidden knowledge-based techniques like naïve Bayes, Artificial neural Networks to discover the secret principle of the feature. It makes it a difficult time to predict the data.…”

Section: Related Workmentioning

confidence: 99%

Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

Saranya¹,

Asha²

2023

Intelligent Automation &Amp; Soft Computing

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In today's growing modern world environment, as human food activities are changing, it is affecting human health, thus leading to diseases like cancer. Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death. So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observation, which has become necessary to classify the type in cancer research. The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment. This paper introduces a Maximal Region-Based Candidate Feature Selection (MRCFS) for early risk diagnosing using Soft-Max Feed Forward Neural Classification (SMF2NC) to solve the above problem. The predictive model is based on a different relational feature learning model, which is possessed to candidate selection to reduce the dimensionality. The redundant features are processed marginal weight rates for observing similar features' variants and the absolute value. Softmax neural hidden layers are trained using the Sigmoid Activation Function (SAF) to create the logical condition for feed-forward layers. Further, the maximal features are introduced to invite a deep neural network constructed on the Feed Forward Recurrent Neural Network (FFRNN). The classifier produces higher classification accuracy than the previous methods and observes the cancer detection, which is recommended for early diagnosis.

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Section: Related Workmentioning

confidence: 99%

Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

Saranya¹,

Asha²

2023

Intelligent Automation &Amp; Soft Computing

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“…This model is used as a model for many pharmacodynamics mechanisms [8]. The PD model of the drug is assumed to be a static nonlinear relation expressed by relation 7 [10].…”

Section: Pharmacodynamics Model (Pd)mentioning

confidence: 99%

“…Where 𝛿 is the sampling distance and 𝑐 𝑙𝑖𝑚 the maximum at which there is no drug resistance. Given that drug resistance r affects the following, 𝐶 50 can be used as a relation (9) in which 𝑘 𝑟 is a parameter related to the disease's ability to develop drug resistance [10]. This method can overcome the limitations of other methods, such as wavelet transform.…”

Section: Pharmacodynamics Model (Pd)mentioning

confidence: 99%

“…In [9], a dose of optimal chemotherapy is calculated by solving the problem of convex optimization based on the inequality of the linear matrix. In [10], it shows that when system states are not fully measurable, MPC is still a useful method of treating cancer. In [11], MPC has been used to provide a chemotherapy program for mice with breast cancer.…”

Section: Introductionmentioning

confidence: 99%

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Treatment of Bone Marrow Cancer Based on Model Predictive Control

Salajegheh¹,

Mojalal

Ghahfarokhi

2021

IJRCS

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Bone marrow is a spongy tissue that contains stem cells that are found inside some bones, including the hip and femur. Bone marrow cancer is a type of cancer that is caused by stem cells that make up the blood cells in the bone marrow. Sometimes these cells grow too fast or abnormally, which is called bone marrow cancer. Bone tissue cells are mainly composed of osteoblasts and osteoclasts. Osteoblast cells constantly build new bone throughout the life of each bone, and other cells called osteoclasts constantly absorb pieces of bone, so the bone is constantly being renewed. In this paper, mathematical models of tumors, the effect of the body on the drug, and the drug on the body are introduced, and then the appropriate dose of the drug to reduce tumor density is calculated using the model predictive control (MPC) algorithm. To obtain an adaptive MPC strategy, the extended least squares (ELS) method developed to learn the parameters of the tumor growth model is used. Finally, the simulation in MATLAB, assuming the model is correct, shows that the tumor is gone, and the bone mass improves over a period of time. The results demonstrate that the proposed method is effective for the treatment of bone marrow cancer.

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“…Mathematical models of cancer are commonly developed on the basis of ordinary differential equations or partial differential equations [9,10,[13][14][15][16][17]. There are several articles that use optimal control theory to control drug dosage in various models of melanoma [18][19][20]. The most popular model is natural competition between susceptible and resistant cancer cells [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Melanoma Treatment Protocols for a Bilinear Control Model

Хайлов

Grigorieva

2023

Mathematics

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In this research, for a given time interval, which is the general period of melanoma treatment, a bilinear control model is considered, given by a system of differential equations, which describes the interaction between drug-sensitive and drug-resistant cancer cells both during drug therapy and in the absence of it. This model also contains a control function responsible for the transition from the stage of such therapy to the stage of its absence and vice versa. To find the optimal moments of switching between these stages, the problem of minimizing the cancer cells load both during the entire period of melanoma treatment and at its final moment is stated. Such a minimization problem has a nonconvex control set, which can lead to the absence of an optimal solution to the stated minimization problem in the classes of admissible modes traditional for applications. To avoid this problem, the control set is imposed to be convex. As a result, a relaxed minimization problem arises, in which the optimal solution exists. An analytical study of this minimization problem is carried out using the Pontryagin maximum principle. The corresponding optimal solution is found in the form of synthesis and may contain a singular arc. It shows that there are values of the parameters of the bilinear control model, its initial conditions, and the time interval for which the original minimization problem does not have an optimal solution, because it has a sliding mode. Then for such values it is possible to find an approximate optimal solution to the original minimization problem in the class of piecewise constant controls with a predetermined number of switchings. This research presents the results of the analysis of the connection between such an approximate solution of the original minimization problem and the optimal solution of the relaxed minimization problem based on numerical calculations performed in the Maple environment for the specific values of the parameters of the bilinear control model, its initial conditions, and the time interval.

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Finite-Set Model Predictive Control of Melanoma Cancer Treatment Using Signaling Pathway Inhibitor of Cancer Stem Cell

Cited by 11 publications

References 39 publications

Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

Treatment of Bone Marrow Cancer Based on Model Predictive Control

Optimal Melanoma Treatment Protocols for a Bilinear Control Model

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