The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the pr...The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented system.The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation(LMB),named the ANNs-LMB.This technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference solution.Fifteen numbers neurons have been used to solve the fractional-order chaotic financial model.The selection of the data to solve the fractional-order chaotic financial model are selected as 75%for training,10%for testing,and 15%for certification.The results indicate that the presented approximate solutions fit exactly with the reference solution and the method is effective and precise.The obtained results are testified to reduce the mean square error(MSE)for solving the fractional model and verified through the various measures including correlation,MSE,regression histogram of the errors,and state transition(ST).展开更多
The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure.The stochastic procedures mainly d...The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure.The stochastic procedures mainly depend on the combination of the artificial neural network(ANNs)along with the Levenberg-Marquardt Backpropagation(LMB)i.e.,ANNs-LMB technique.The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional orderα.The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1.The data proportion is applied as 73%,15%,and 12%for training,testing,and certification to solve the chaotic fractional system.The acquired results are verified through the comparison of the reference solution,which indicates the proposed technique is efficient and robust.The 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error(MSE).To authenticate the exactness,and consistency of the technique,the obtained performances are plotted in the figures of correlation measures,error histograms,and regressions.From these figures,it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.展开更多
The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model(NSM)using the supervised neural networks(SNNs)along with the novel features of Levenberg-Marquardt backpr...The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model(NSM)using the supervised neural networks(SNNs)along with the novel features of Levenberg-Marquardt backpropagation technique(LMBT),i.e.,SNNs-LMBT.The SNNs-LMBT is implemented with three different types of sample data,authentication,testing and training.The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75%for training,15%for validation and 10%for testing,respectively.For the numerical measures of the nonlinear dynamics of the NSM,the Runge-Kutta scheme is implemented to form the reference dataset.The attained numerical form of the nonlinear dynamics of the NSM through the SNNs-LMBT is implemented in the reduction of the mean square error(MSE).For the exactness,competence,reliability and efficiency of the proposed SNNs-LMBT,the numerical actions are capable using the proportional arrangements through the features of the MSE results,error histograms(EHs),regression and correlation.展开更多
High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications,offering a more comprehensive understanding of biological pro...High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications,offering a more comprehensive understanding of biological processes and diseases.Omics reference materials play a pivotal role in ensuring the accuracy,reliability,and comparability of laboratory measurements and analyses.However,the current application of omics reference materials has revealed several issues,including inappropriate selection and underutilization,leading to inconsistencies across laboratories.This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics,encompassing(epi-)genomics,transcriptomics,proteomics,and metabolomics.By summarizing their characteristics,advantages,and limitations along with appropriate performance metrics pertinent to study purposes,we provide an overview of how omics reference materials can enhance data quality and data integration,thus fostering robust scientific investigations with omics technologies.展开更多
Homologous recombination deficiency(HRD)has emerged as a critical prognostic and predictive biomarker in oncology.However,current test-ing methods,especially those reliant on targeted panels,are plagued by inconsisten...Homologous recombination deficiency(HRD)has emerged as a critical prognostic and predictive biomarker in oncology.However,current test-ing methods,especially those reliant on targeted panels,are plagued by inconsistent results from the same samples.This highlights the urgent need for standardized benchmarks to evaluate HRD assay performance.In phases lla and Ilb of the Chinese HRD Harmonization Project,we de-veloped ten pairs of well-characterized DNA reference materials derived from lung,breast,and melanoma cancer cell lines and their matched normal cell lines,keeping each paired with seven cancer-to-normal mass ratios.Reference datasets for allele-specific copy number variations(AsCNVs)and HRD scores were established and validated using three sequencing methods and nine analytical pipelines.The genomic instabil-ity scores(GISs)of the reference materials ranged from 11 to 96,enabling validation across various thresholds.The AsCNV reference datasets covered a genomic span of 2340 to 2749 Mb,equivalent to 81.2%to 95.4%of the autosomes in the 37d5 reference genome.These bench-marks were subsequently utilized to assess the accuracy and reproducibility of four HRD panel assays,revealing significant variability in both ASCNV detection and HRD scores.The concordance between panel-detected GISs and reference GISs ranged from 0.81 to 0.94,with only two assays exhibiting high overall agreement with Myriad MyChoice CDx for HRD classification.This study also identified specific challenges in ASCNV detection in HRD-related regions and the profound impact of high ploidy on consistency.The established HRD reference materials and datasets providea robust toolkit forobjective evaluation of HRD testing.展开更多
基金This research received funding support from the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation(Grant Number B05F640088).
文摘The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented system.The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation(LMB),named the ANNs-LMB.This technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference solution.Fifteen numbers neurons have been used to solve the fractional-order chaotic financial model.The selection of the data to solve the fractional-order chaotic financial model are selected as 75%for training,10%for testing,and 15%for certification.The results indicate that the presented approximate solutions fit exactly with the reference solution and the method is effective and precise.The obtained results are testified to reduce the mean square error(MSE)for solving the fractional model and verified through the various measures including correlation,MSE,regression histogram of the errors,and state transition(ST).
基金National Research Council of Thailand(NRCT)and Khon Kaen University:N42A650291.
文摘The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure.The stochastic procedures mainly depend on the combination of the artificial neural network(ANNs)along with the Levenberg-Marquardt Backpropagation(LMB)i.e.,ANNs-LMB technique.The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional orderα.The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1.The data proportion is applied as 73%,15%,and 12%for training,testing,and certification to solve the chaotic fractional system.The acquired results are verified through the comparison of the reference solution,which indicates the proposed technique is efficient and robust.The 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error(MSE).To authenticate the exactness,and consistency of the technique,the obtained performances are plotted in the figures of correlation measures,error histograms,and regressions.From these figures,it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.
文摘The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model(NSM)using the supervised neural networks(SNNs)along with the novel features of Levenberg-Marquardt backpropagation technique(LMBT),i.e.,SNNs-LMBT.The SNNs-LMBT is implemented with three different types of sample data,authentication,testing and training.The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75%for training,15%for validation and 10%for testing,respectively.For the numerical measures of the nonlinear dynamics of the NSM,the Runge-Kutta scheme is implemented to form the reference dataset.The attained numerical form of the nonlinear dynamics of the NSM through the SNNs-LMBT is implemented in the reduction of the mean square error(MSE).For the exactness,competence,reliability and efficiency of the proposed SNNs-LMBT,the numerical actions are capable using the proportional arrangements through the features of the MSE results,error histograms(EHs),regression and correlation.
基金supported in part by Shanghai Sailing Program(22YF1403500)the National Natural Science Foundation of China(32300536,31720103909 and 32170657)+2 种基金the National Key R&D Project of China(2018YFE0201603 and 2018YFE0201600)State Key Laboratory of Genetic Engineering(SKLGE-2117)the 111 Project(B13016).
文摘High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications,offering a more comprehensive understanding of biological processes and diseases.Omics reference materials play a pivotal role in ensuring the accuracy,reliability,and comparability of laboratory measurements and analyses.However,the current application of omics reference materials has revealed several issues,including inappropriate selection and underutilization,leading to inconsistencies across laboratories.This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics,encompassing(epi-)genomics,transcriptomics,proteomics,and metabolomics.By summarizing their characteristics,advantages,and limitations along with appropriate performance metrics pertinent to study purposes,we provide an overview of how omics reference materials can enhance data quality and data integration,thus fostering robust scientific investigations with omics technologies.
基金supported by the National Key R&D Program of China(Grant No.2022YFF1202203)the NIFDC Fund for Key Technology Research,China(Grant No.GJJS-2022-2-1).
文摘Homologous recombination deficiency(HRD)has emerged as a critical prognostic and predictive biomarker in oncology.However,current test-ing methods,especially those reliant on targeted panels,are plagued by inconsistent results from the same samples.This highlights the urgent need for standardized benchmarks to evaluate HRD assay performance.In phases lla and Ilb of the Chinese HRD Harmonization Project,we de-veloped ten pairs of well-characterized DNA reference materials derived from lung,breast,and melanoma cancer cell lines and their matched normal cell lines,keeping each paired with seven cancer-to-normal mass ratios.Reference datasets for allele-specific copy number variations(AsCNVs)and HRD scores were established and validated using three sequencing methods and nine analytical pipelines.The genomic instabil-ity scores(GISs)of the reference materials ranged from 11 to 96,enabling validation across various thresholds.The AsCNV reference datasets covered a genomic span of 2340 to 2749 Mb,equivalent to 81.2%to 95.4%of the autosomes in the 37d5 reference genome.These bench-marks were subsequently utilized to assess the accuracy and reproducibility of four HRD panel assays,revealing significant variability in both ASCNV detection and HRD scores.The concordance between panel-detected GISs and reference GISs ranged from 0.81 to 0.94,with only two assays exhibiting high overall agreement with Myriad MyChoice CDx for HRD classification.This study also identified specific challenges in ASCNV detection in HRD-related regions and the profound impact of high ploidy on consistency.The established HRD reference materials and datasets providea robust toolkit forobjective evaluation of HRD testing.
