Claims:
1. A computer implemented method of cancer model for the diagnose and treatment of the cancer, the computer implemented method is the computer implemented method comprising steps of :
Preparing a dynamic cell model, by the processor of the computing device , wherein the dynamic cell model comprises one and more molecular activity as one or more parameters with a parameters of interaction between the molecular activity of the cell with tissue cells, tumour cells, and activated immune system as one or more mathematical equations which substantially represent the dynamic state of the cell, the or each mathematical equations comprises A derivative of Caputo , A derivative of both Caputo and Fabrizio , & a AB fractional derivative;
Generating variations of the parameters of the mathematical equations with a fractional derivative which incorporates the interactions between healthy tissue cells, tumour cells, and activated immune system, by the processor of the computing device, wherein variation of one or more input functions which can be used to generate the variations of the mathematical equation;
Modifying, by the processor of the computing device , one or each of the mathematical equations of the model to represent a one or more changes caused by the cancer to the molecular interactions;
Determining, by the processor of the computing device an initial condition of cancer model by an iterative process until the initial conditions meet a predetermined performance requirement;
Solving the one or more equations , by the processor of the computing device, to produce a plurality of solutions which represent of one or more selected molecular species based on one or more input functions to determine the effect of the input function on the dynamic state of the cell;
Performing a test of the solutions, by the processor of the computing device, to determine a best solution among the saluting , which combatable to a predetermined requirement; and
Selecting the best solution, by the processor of the computing device, to demonstrate the effect of the input functions on the dynamic state of the cell with respect to the cancer.

2. The computer implemented method of cancer model for the diagnose and treatment of the cancer as claimed in claim 1, the computer implemented method is processed by at least one processor of the at least one computing device.

3. The computer implemented method of cancer model for the diagnose and treatment of the cancer as claimed in claim 1, the computer implemented method is processed by the cloud computing.
, Description:FIELD OF INVENTION

[001]. The present invention related to the technical field of biological disease modeling, and in particular relates to cancer modeling method and use thereof.
[002]. The present invention generally relates to computational models of cancer prediction, diagnose and treatment. Particularly, the present invention relates to computational biology modeling using mathematical derivative to derive a model for simulation or systematic analyses of cancer with respect to an individual organisms and life forms.
[003]. More particularly, the present invention is related to a computer implemented method of cancer model for the cancer treatment with using different mathematical operators...

BACKGROUND & PRIOR ART

[001]. Cancer could be a growth disease, wherever cells are reprogrammed to avoid the checkpoints in control of nutrient aging, growth, supply, dissemination, and death. One in eight human deaths is caused by cancer. The cancer treatment relies on the kind of cancer and also the treatment involves one or additional of the subsequent components: surgery, chemotherapy, radiation therapy, and immune therapy, or combination of two or additional of those treatments.
[002]. The modeling of cancer disease with using effect of the various biological and diagnostic parameter presented in the prior art. Some of mathematical models for the cancer is presented with the objective of the predictive, diagnose and treatment of the cancer. Some of the prior work is listed herewith:
[003]. RU2493770C2 - Method Of Diagnosing Cancer In Patients With Pre-Operation Cytological Diagnosis "Follicular Neoplasm" Of Thyroid Gland By Means Of Mathematical Modeling presents medicine, namely to oncology and surgery, and can be used for diagnostics of thyroid gland cancer in patients with pre-operation cytological diagnosis "follicular neoplasm". Cytological, ultrasound and clinical-anamnestic examination of thyroid gland is carried out. 13 parameters, constituting mathematical model, are determined. Parameters are introduced into mathematical model, in case if sign is present, value "1" is given, in case of its absence "0". Values Y1 and Y2 are calculated by claimed function. If Y1 is larger than Y2, diagnosis of thyroid gland cancer is stated. If Y2 is larger, than Y1, diagnosis of thyroid gland cancer is excluded. [0064] EFFECT: method makes it possible to diagnose cancer of thyroid gland in pre-operation period for selection of adequate type of surgery.
[004]. CN105868576A - Mathematic model and method for predicting postoperative short-term reoccurrence transition probability of huge hepatic cancer patient presents a mathematic model and a method for predicting postoperative short-term reoccurrence transition probability of a huge hepatic cancer patient. The mathematic model adopts P= (P1+P2+P3+P4)*A2/4, wherein P1 is the multi-factor predicting probability; P2 is the percentage of immune inflammatory factor exceeding the normal range; P3 is the percentage of polymorphic bands of mononucleotide; P4 is the gene expression for predicting the postoperative short-term reoccurrence transition probability of the huge hepatic cancer patient; A is the ratio of maximums and minimums of P1, P2, P3 and P4. The mathematic model has the advantages that multiple related indexes of the postoperative short-term reoccurrence transition of the huge hepatic cancer are comprehensively analyzed by multiple factors and proteins, the effect of predicting the postoperative short-term reoccurrence transition is realized by a predicting model, and the significant meaning is realized for the individual selection of clinical practices and therapy schemes.
[005]. CN107622800A - Mathematical model for predicting therapeutic effect of colorectal cancer liver metastasis presents s a mathematical model for predicting a therapeutic effect of colorectal cancer liver metastasis. The model is Pet=expel(Y<^>Cat)/(1+exp(Y<^>Cet)), wherein, Y<^>Cet=1.516-0.165XA-0.726XC-1.140XE-0.944XL-0.477XR+0.821XP. According to the mathematical model for predicting the therapeutic effect of colorectal cancer liver metastasis, after screening a population sensitiveto cetuximab, the objective response rate is increased significantly, and thus the model can be used for the prediction of a treatment mode well.
[006]. KR101224472B1 - Modeling Method And Apparatus For Behavior Of Cancer Cell presents summing flow rate of cancer cells for chip s to inhibit cancer cell total flow rate is calculated, said total flow rate and mass of cancer cells coupled relations among the pixel of preserving the nonlinear diffusion equations and, said value using nonlinear diffusion equation for regulating the process be at 500. In addition air cells behavior of cancer cells have to be considered influence degree query cell air which also includes process for modeling behavior query, query behavior air cells-induced cell diffusion process for modeling air query flow rate is calculated, said air induction of apoptosis caused by diffusion preserving mass velocity query coupled relations among the pixel of the nonlinear diffusion equations and, said value using nonlinear diffusion equation for regulating the process be at 500. Modeling behavior cancer said method produced by the model behavior of s to inhibit cancer cell using one can analyze-dimensionally and 3, shape change of cancer cells can be user input.’
[007]. IN3169DEL2014A - Artificial Intelligence Based Modeling Of Liver Cancergrowth Investigation presents liver cancer growth can be governed by concept learning and artificial neural modeling. The progress of treatment of liver cancer based on past event (intensities of cancer growth at specific observed timing instants) can be computed on the basis of neuro-associator. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition.
[008]. WO2018037137A1 - Simulation And Patient-Specific Scale Tissue Modeling Of The Growth Of Prostate Cancer presents a simulation of the evolution of a tumour in the prostate gland of a subject is based on at least one coupled system of reaction-diffusion equation and a patient-specific geometric model of the prostate gland of the subject. The use of reaction-diffusion equations and a patient-specific geometric model provides a tumour model that predicts the expected progress of prostate cancer in the subject. The tumour model can be used to design a treatment adapted to the subject.
[009]. CN107273717A - Lung cancer serum gene detection model and construction method and application thereof presents a lung cancer serum gene detection model, which consists of five polypeptides. m/z obtained by MALDI-TOF-MS (Matrix-Assisted Laser Desorption/ Ionization Time of Flight Mass Spectrometry) analysis is respectively 4092.4Da, 4585.05Da, 1365.1Da, 4643.49Da and 4438.43Da. The invention also discloses a construction method for the above detection model. The lung cancer serum gene detection model only needs 5 microlitres of serum for carrying out EGFR (Epidermal Growth Factor Receptor) gene mutation state evaluation, is low in cost, is quick and can basically realize automatic operation. In addition, the sensitivity of the detection model can achieve tissue ARMS (Amplification Refractory Mutation System) gene detection sensitivity, and detection accuracy is greatly improved.
[0010]. CN110391025A - A micro-multi-dimensional gastric cancer early risk assessment of artificial intelligent modeling method presents cancer prognosis of patients is the impact on the key. For high-risk groups for early assessment and early warning, for the promotion of cancer prevention and early diagnosis of significant scientific value and practical significance. The invention provides a [...] micro-multi-dimensional gastric cancer early risk assessment of artificial intelligent modeling method, can be comprehensive individual exposed by the multi-gastric cancer risk factors and western medicine phenotype, from the "gene - molecular - cell - system - biological individual" macro - micro-multi-dimension and the stomach cancer risk simulation and evaluation, quantitative calculate the testee cancer risk; and can be different areas the characteristics of stomach cancer risk factors, carry on the individualized with an accurate assessment of the early warning. The invention uses the 12961 part of clinical cases to carry out the verification, the result shows that the algorithm can effectively predicts the risk of gastric cancer, for improving the early treatment level of stomach cancer has important clinical significance.
[0011]. US20130191098A1 - Methods And Systems For Simulations Of Complex Biological Networks Using Gene Expression Indexing In Computational Models presents method has been developed for using genome-wide transcription profile (i.e., gene-expression level) values to derive a gene expression index used as a kinetic value for every biological reaction and process assigned to each and every gene. This kinetic value is used in computational biology programs, i.e., mathematical models integrating genome, transcriptome, proteome, reactome, fluxome, metabolome, physiome, and phenome, in any combination, for simulations or theoretical systematic analyses of all life forms. This approach allows a model to be generated for any individual organism at any state of life, health condition, or disease/traumatic process. The model can include any or all biological reactions and processes, because an exact kinetic value becomes available; and, thereby, the outcomes represent stable or dynamic states of the individual organism at the time the biological specimen or sample was collected.
[0012]. CN104200060A - Model and method for predicting probability of post-operation recent relapse and metastasis of giant liver caner of a patient presents a mathematical model and method for predicting the probability of post-operation recent relapse and metastasis of the giant liver caner of a patient. The mathematical model can be represented through the equation: P=1/(1+Y), wherein P is the probability of the post-operation recent relapse and metastasis of the giant liver caner of the patient; when P represents the probability of relapse and metastasis occurring in six mouths, The mathematical model and method for predicting the probability of post-operation recent relapse and metastasis of the giant liver caner of the patient mainly have the advantages that multiple indexes relevant to recent relapse and metastasis of the giant liver cancer are comprehensively analyzed based on multiple factors and polyprotein.
[0013]. Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.
[0014]. As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0015]. The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.
[0016]. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[0017]. The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.

SUMMARY
[0018]. The present invention mainly cures and solves the technical problems existing in the prior art. In response to these problems, the present invention provides a computer implemented method of cancer model for the cancer treatment with using different mathematical operators.
[0019]. The present disclosure presents a computer implemented method of cancer model for the diagnose and treatment of the cancer, the computer implemented method is the computer implemented method comprising steps of preparing a dynamic healthy cell model, by the processor of the computing device , wherein the dynamic cell model comprises one and more molecular activity as one or more parameters with a parameters of interaction between the molecular activity of the cell with healthy tissue cells, tumour cells, and activated immune system as one or more mathematical equations which substantially represent the dynamic state of the cell, the or each mathematical equations comprises A derivative of Caputo , A derivative of both Caputo and Fabrizio , & a AB fractional derivative; Solving the one or more equations , by the processor of the computing device, to produce a plurality of solutions which represent of one or more selected molecular species based on one or more input functions to determine the effect of the input function on the dynamic state of the cell; Performing a test of the solutions, by the processor of the computing device, to determine a best solution among the saluting , which combatable to a predetermined requirement; and Selecting the best solution, by the processor of the computing device, to demonstrate the effect of the input functions on the dynamic state of the cell with respect to the cancer.

OBJECTIVE OF THE INVENTION

[0020]. The principle objective of the present invention is to provide a computer implemented method of cancer model for the cancer treatment with using different mathematical operators.
[0021]. Further objective of the present invention is to solve the problems of techniques of the prior arts in design of cancer models.

BRIEF DESCRIPTION OF DRAWINGS

[0022]. Further clarify various aspects of some example embodiments of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It is appreciated that these drawings depict only illustrated embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
[0023]. In order that the advantages of the present invention will be easily understood, a detailed description of the invention is discussed below in conjunction with the appended drawings, which, however, should not be considered to limit the scope of the invention to the accompanying drawings, in which:
[0024]. Figure 1 shows a flow diagram of a computer implemented method of cancer model for the cancer treatment with using different mathematical operator, according to the present invention.
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DETAIL DESCRIPTION

[0025]. The present invention disclosures design of a computer implemented method of cancer model for the cancer treatment with using different mathematical operators.
[0026]. Figure 1 shows an exemplary design of design of a computer implemented method of cancer model for the cancer treatment with using different mathematical operators.
[0027]. Although the present disclosure has been described with the purpose of design of a computer implemented method of cancer model for the cancer treatment with using different mathematical operators, it should be appreciated that the same has been done merely to illustrate the invention in an exemplary manner and to highlight any other purpose or function for which explained structures or configurations could be used and is covered within the scope of the present disclosure.
[0028]. Embodiments of the present disclosure include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, firmware, and/or by human operators.
[0029]. The computer implemented method of cancer model for the diagnose and treatment of the cancer is processed by at least one processor of at least one computing device . Preferably, The computer implemented method is processed by the cloud computing..
[0030]. The computer implemented method comprising steps of preparing a dynamic healthy cell model by the processor of the computing device.
[0031]. The dynamic cell model comprises one and more molecular activity as one or more parameters with parameters of interaction between the molecular activity of the cell with healthy tissue cells, tumour cells, and activated immune system as one or more mathematical equations which substantially represent the dynamic state of the cell.
[0032]. Each mathematical equations of the model comprises A derivative of Caputo , A derivative of both Caputo and Maurizio , & a AB fractional derivative.
[0033]. Then variations of the parameters of the mathematical equations is generated with a fractional derivative which incorporates the interactions between healthy tissue cells, tumour cells, and activated immune system, by the processor of the computing device. The variation of one or more input functions which can be used to generate the variations of the mathematical equation.
[0034]. one or each of the mathematical equations of the model is modified by by the processor of the computing device using a one or more changes caused by the cancer to the molecular interactions.
[0035]. The processor of the computing device determines an initial condition of cancer model by an iterative process until the initial conditions meet a predetermined performance requirement.
[0036]. The mathematical function is solved by by the processor of the computing device, to produce a plurality of solutions which represent of one or more selected molecular species based on one or more input functions to determine the effect of the input function on the dynamic state of the cell.
[0037]. A test of the solutions is performed by the processor of the computing device to determine a best solution among the saluting , which combatable to a predetermined requirement.
[0038]. And and the final step of the method the best solution is selected by the processor of the computing device to demonstrate the effect of the input functions on the dynamic state of the cell with respect to the cancer.
[0039]. Embodiments of the present disclosure may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The machine readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).
[0040]. The term “computing device” or “computer-readable storage medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A machine-readable medium may include a non-transitory medium in which data can be stored, and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a no transitory medium may include but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or versatile digital disk (DVD), flash memory, memory or memory devices..