https://sifisheriessciences.com/index.php/journal/article/view/1280
Bibliographic record
Abstract
Biosensors are powerful analytical devices that detect and quantify target analytes in a sample. Due to their high selectivity and sensitivity, enzymes, proteins, antibodies, peptides, and whole cells are commonly used as sensing elements in biosensors. However, the design and optimization of biosensors can be challenging due to the complexity of these biomolecules and their interactions with target analytes. In recent years, computational methods have emerged as powerful tools for designing and optimizing biosensors, enabling researchers to predict the behavior of biomolecules and their interactions with target analytes. Computational fluid mechanics can aid in the design of microfluidic systems for biosensing applications. In contrast, molecular dynamic simulation, molecular docking, quantum mechanics, and virtual screening methods can be used to predict the behavior of biomolecules at the atomic level and study the binding kinetics and thermodynamics of interactions. This paper critically discusses the use of computational methods in biosensors, focusing on enzyme-based, protein-based, antibody-based, peptide-based, and whole-cell-based biosensors. We also review using computational fluid mechanics, molecular dynamic simulation, molecular docking, quantum mechanics, and virtual screening methods in biosensor design and optimization. Additionally, we discuss the applications of these computational methods and biosensors in healthcare, environmental monitoring, food safety, biodefense, and security. Combining computational biosensors and computational methods offers tremendous potential for developing advanced biosensors with enhanced sensitivity, specificity, and accuracy. However, challenges remain, such as the need for more accurate models and the integration of experimental and computational approaches. We conclude by discussing the prospects and challenges of computational biosensors and methods, highlighting the need for further research to drive innovation and improve human health and well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.654 | 0.702 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".