ReviewSensors (Basel, Switzerland)2021
Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed.
- Surface Plasmon Resonance Imaging and Microscopy Modalities for Information-Rich, Label-Free Analysis of Biomolecular Interactions and Disease Biomarkers.Chemical & biomedical imaging · 2026Review
- Synthetic Microbial Community Biosensors: From Engineered Ecosystems to Modular Detection Platforms with AI-Driven Intelligence.Biosensors · 2026Review
- State-of-the-Art and Next Generation Intra-Articular Implantable Biosensors for Osteoarthritis: From Analytical Limits to Operational Stability.Biosensors · 2026Review
- LPFG Biosensor for IL-6 Detection in Murine Serum Samples Associated with Ischemic Disease.Sensors (Basel, Switzerland) · 2026Article
- Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies.ACS omega · 2026Review
- Recent Advances in Raman Spectral Classification with Machine Learning.Sensors (Basel, Switzerland) · 2026Review
- Advances in Impedimetric Biosensors: Current Applications and Future Directions.Micromachines · 2025Review
- Accuracy Enhancement in Refractive Index Sensing via Full-Spectrum Machine Learning Modeling.Biosensors · 2025Article
- Colorimetric Biosensors: Advancements in Nanomaterials and Cutting-Edge Detection Strategies.Biosensors · 2025Review
- Machine Learning Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing.ACS omega · 2025Article
- Preliminary Study on Sensor-Based Detection of an Adherent Cell's Pre-Detachment Moment in a MPWM Microfluidic Extraction System.Sensors (Basel, Switzerland) · 2025Article
- Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives.Diagnostics (Basel, Switzerland) · 2025Review
- Biosensors integrated within wearable devices for monitoring chronic wound status.APL bioengineering · 2025Article
- An explainable deep learning framework for biosensing data interpretation in biomedical engineering and real-time health diagnostics.Frontiers in bioengineering and biotechnology · 2025Article
- Applications and prospects of biomaterials in diabetes management.Frontiers in bioengineering and biotechnology · 2025Review
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.Biosensors · 2024Review
- The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.JAMIA open · 2024Review
- Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2024Review
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Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor's signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.