Harnessing machine vision and image processing in multicolor lateral flow assays for cutting-edge liquid biopsy applications

Sensors and Actuators B Chemical, Journal Year: 2025, Volume and Issue: unknown, P. 137729 - 137729

Published: April 1, 2025

Language: Английский

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Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness these sensors, it crucial to find right balance among Researchers and engineers continually explore innovative approaches sensitivity, selectivity, reliability. Machine learning (ML) techniques facilitate analysis predictive modeling sensor establishing quantitative relationships between parameters their effects. This work presents a case study on developing molecularly imprinted polymer (MIP)-based for detecting doxorubicin (Dox), emphasizing use ML-based ensemble models improve Four ML models, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), are used evaluate effect each parameter prediction performance, using SHapley Additive exPlanations (SHAP) method determine feature importance. Based analysis, removing less influential introducing new significantly improved model's capabilities. By applying min-max scaling technique, ensured that all features contribute proportionally model process. Additionally, multiple models─Linear Regression (LR), KNN, DT, RF, Adaptive (AdaBoost), (GB), Support Vector (SVR), XGBoost, Bagging, Partial Least Squares (PLS), Ridge Regression─are applied data set in predicting output current compared. further novel proposed integrates GB, Bagging regressors, leveraging combined strengths offset individual weaknesses. main benefit this lies its ability MIP-based stacking regressor model, which improves methodology broadly applicable development other with different transducers sensing elements. Through extensive simulation results, demonstrated superior compared models. achieved an R-squared (R2) 0.993, reducing root-mean-square error (RMSE) 0.436 mean absolute (MAE) 0.244. These improvements enhanced sensitivity reliability sensor, demonstrating substantial gain over

Language: Английский

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Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

Analytical and Bioanalytical Chemistry, Journal Year: 2023, Volume and Issue: 416(9), P. 2261 - 2275

Published: Dec. 20, 2023

Language: Английский

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Top 20 Influential AI-Based Technologies in Chemistry

Published: April 12, 2024

The beginning and ripening of digital chemistry is analyzed focusing on the role artificial intelligence (AI) in an expected leap chemical sciences to bring this area next evolutionary level. analytic description selects highlights top 20 AI-based technologies 7 broader themes that are reshaping field. It underscores integration tools such as machine learning, big data, twins, Internet Things (IoT), robotic platforms, smart control processes, virtual reality blockchain, among many others, enhancing research methods, educational approaches, industrial practices chemistry. significance study lies its focused overview how these innovations foster a more efficient, sustainable, innovative future sciences. This article not only illustrates transformative impact but also draws new pathways chemistry, offering broad appeal researchers, educators, industry professionals embrace advancements for addressing contemporary challenges

Language: Английский

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Highly specific detection of ROR1 cancer biomarker with bipolar electrochemiluminescence

Microchimica Acta, Journal Year: 2024, Volume and Issue: 191(12)

Published: Nov. 8, 2024

Language: Английский

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