A Multi-State Optimization Framework for Parameter Estimation in Biological Systems

Xu, Gu

doi:10.1109/tcbb.2015.2459686

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…These Gene Expressions have been usually used for Gene Pattern Classifications. From the available literature survey [1][2][3][4][5][6], it was noticed that the Data Mining Techniques are facilitating to classify and predict various Cancer Gene Patterns. The Classifiers are used to classify microarray samples for pattern classification.…”

Section: Introductionmentioning

confidence: 99%

“…ie the normal microarray sample data set and cancer pattern samples can be classified with the help of Classifiers [12][13][14][15][16]. If the samples had a few subtypes of cancer pattern, then we needed multiclass cancer pattern classifiers [1][2][3][4]. From the literature survey, it was noticed that the Multi-Class Cancer Pattern Classifier can be employed to improve the classification accuracy [17].…”

Section: Introductionmentioning

confidence: 99%

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EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Subasree

Gopalan²,

Sakthivel

2018

IJET

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Microarray based Cancer Pattern Classification is one of the popular techniques in Bioinformatics Research. This Research Work is noticed that for studying the expression levels through the Gene Expression profiling experiments, thousands of Genes have to be simultaneously studied to understand the patterns of the Gene Expression or Cancer Pattern. This research work proposed an efficient Cancer Pattern Clas-sifier called An Enhanced Multi-Objective Pswarm (EMOPS) and it is studied thoroughly in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. The results were compared with the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this work considered a few cancer patterns namely Bladder, Breast, Colon, Endometrial, Kidney, Leukemia, Lung, Melanoma, Mom-Hodgkin, Pancreatic, Prostate and Thyroid. From our experimental results, it was noticed that the proposed model outperforms the identified three classifiers in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. To improve the performance of the system further in term of Processing Time, the proposed model Enhanced Multi-Objective Pswarm (EMOPS) is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time decreases considerably which will improve the performance of the Proposed Model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Subasree

Gopalan²,

Sakthivel

2018

IJET

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“…Frequently, the problem of biological parameter estimation are said to be NP hard, so they are multi-modal and ill conditioned i.e. there are more than one true solutions for the estimated parameters that fit the model and produce the time course information [ 2 ]. Many algorithms have been developed to address this problem.…”

Section: Introductionmentioning

confidence: 99%

A modified particle swarm optimization algorithm for parameter estimation of a biological system

Mosayebi

Bahrami

2018

Theor Biol Med Model

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BackgroundMathematical modeling has achieved a broad interest in the field of biology. These models represent the associations among the metabolism of the biological phenomenon with some mathematical equations such that the observed time course profile of the biological data fits the model. However, the estimation of the unknown parameters of the model is a challenging task. Many algorithms have been developed for parameter estimation, but none of them is entirely capable of finding the best solution. The purpose of this paper is to develop a method for precise estimation of parameters of a biological model.MethodsIn this paper, a novel particle swarm optimization algorithm based on a decomposition technique is developed. Then, its root mean square error is compared with simple particle swarm optimization, Iterative Unscented Kalman Filter and Simulated Annealing algorithms for two different simulation scenarios and a real data set related to the metabolism of CAD system.ResultsOur proposed algorithm results in 54.39% and 26.72% average reduction in root mean square error when applied to the simulation and experimental data, respectively.ConclusionThe results show that the metaheuristic approaches such as the proposed method are very wise choices for finding the solution of nonlinear problems with many unknown parameters.

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Agent-based approaches for biological modeling in oncology: A literature review

Stephan,

Galland,

Labbani Narsis

et al. 2024

Artificial Intelligence in Medicine

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A Multi-State Optimization Framework for Parameter Estimation in Biological Systems

Cited by 3 publications

References 54 publications

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

A modified particle swarm optimization algorithm for parameter estimation of a biological system

Agent-based approaches for biological modeling in oncology: A literature review

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