Source 1primary paper2026MED
Toolkit/artificial neural network (ANN)
artificial neural network (ANN)
Also known as: ANN
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were proposed to model a voltammetric biosensor for plasma miR-155 detection.
Usefulness & Problems
No literature-backed usefulness or problem-fit explainer has been materialized for this record yet.
Published Workflows
Objective: Develop a data-driven framework to model and optimize fabrication parameters for a voltammetric biosensor used for plasma miR-155 detection in breast cancer.
Why it works: The workflow uses a predictive model of how fabrication parameters affect biosensor output, then applies an optimization algorithm to identify parameter settings expected to improve performance while reducing trial-and-error experimentation.
nonlinear relationship between fabrication parameters and biosensor outputartificial neural network modelingadaptive neuro-fuzzy inference system modelinggenetic algorithm optimization
Stages
- 1.Model biosensor output from fabrication parameters(functional_characterization)
This stage replaces labor-intensive trial-and-error optimization with a data-driven model that can estimate biosensor output from fabrication settings.
Selection: predictive modeling of nonlinear relationships between six fabrication parameters and biosensor output current
- 2.Optimize fabrication parameters with genetic algorithm(decision_gate)
This stage uses the learned model to identify optimal fabrication settings without exhaustive experimental search.
Selection: parameter values predicted to optimize biosensor output current
Steps
- 1.Assemble dataset of biosensor output current and six fabrication parameters
Provide training data for modeling the relationship between fabrication settings and biosensor output.
A dataset is required before ANN or ANFIS can be used to model the biosensor response.
- 2.Train and compare ANN and ANFIS modelspredictive models
Model nonlinear relationships between fabrication parameters and biosensor output and identify the better-performing approach.
Model comparison is needed before selecting a predictive framework for optimization.
- 3.Use GA to determine optimal fabrication parameter valuesoptimizer
Search the fabrication parameter space for settings that maximize biosensor output current.
Optimization follows predictive modeling because GA requires a modeled relationship between fabrication parameters and output to guide the search.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Techniques
Computational DesignTarget processes
No target processes tagged yet.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
ANN outperformed ANFIS for modeling the voltammetric plasma miR-155 biosensor.
The results show that the ANN approach outperforms ANFIS, achieving an [Formula: see text] value of 0.9845.
R-squared 0.9845
The ANN-GA framework reduces experimental iterations, lowers material consumption, and enables rapid parameter optimization for biosensor development.
The ANN-GA framework offers a practical and efficient strategy for biosensor development by reducing experimental iterations, thereby lowering material consumption and enabling rapid parameter optimization.
GA identified a set of six fabrication parameter values that produced an output current of 223 nA for the plasma miR-155 biosensor.
The optimal parameter values were 7.12 nM, 85.22 min, 6.54 min, 118.02 min, 0.12 mM, and 93.39 min for detection probe concentration, detection probe incubation time, MCH incubation time, hybridization time, OB concentration, and OB incubation time, respectively, resulting in an output current of 223 nA.
detection probe concentration 7.12 nMdetection probe incubation time 85.22 minhybridization time 118.02 minMCH incubation time 6.54 minOB concentration 0.12 mMOB incubation time 93.39 minoutput current 223 nA
Approval Evidence
1 source2 linked approval claimsfirst-pass slug artificial-neural-network-ann
Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were proposed to model a voltammetric biosensor for plasma miR-155 detection.
Source:
comparative performancesupports
ANN outperformed ANFIS for modeling the voltammetric plasma miR-155 biosensor.
The results show that the ANN approach outperforms ANFIS, achieving an [Formula: see text] value of 0.9845.
Source:
engineering utilitysupports
The ANN-GA framework reduces experimental iterations, lowers material consumption, and enables rapid parameter optimization for biosensor development.
The ANN-GA framework offers a practical and efficient strategy for biosensor development by reducing experimental iterations, thereby lowering material consumption and enabling rapid parameter optimization.
Source:
Comparisons
No literature-backed comparison notes have been materialized for this record yet.
Ranked Citations
- 1.