qPCR Curve Classification Model for DNA Detection Accuracy
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Solution Overview
Problem
Conventional qPCR methods face inaccuracies due to artifacts, baseline drift, thermal noise, and reagent fluctuations, leading to unreliable detection of DNA strand segments, particularly in the presence of pathogens, requiring extensive data sets for model training and increased effort for software-based corrections.
Innovation Solution
A data-based classification model, such as an artificial neural network or support vector machine, is used to evaluate the shape of qPCR curves, allowing for more reliable assessment of DNA strand segment presence by fitting measured curves to parameterized presence or nonpresence functions, reducing misclassifications and the need for extensive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple parallel measurements are carried out to improve accuracy through averaging, then measurement precision is improved, but productivity decreases due to increased time and effort
Solution Approach 1:
The patent creates a digital twin or virtual model of the qPCR measurement process through simulation. Instead of performing multiple physical parallel measurements, the system uses a trained classification model that can evaluate qPCR curves rapidly without requiring repeated experimental runs. This virtual copying approach maintains measurement accuracy while eliminating the time penalty of multiple physical measurements.
Solution Approach 2:
The patent performs preliminary training of the classification model using extensive measurement data before actual qPCR analysis. This preliminary action creates a pre-trained system that can rapidly classify new qPCR curves without requiring multiple parallel measurements during the actual analysis phase. The heavy computational work is done in advance, enabling fast real-time evaluation.
2Measurement precision
If software-based corrections are applied to compensate for artifacts and baseline drift, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the classification model continuously learns from measurement results and adjusts its classification thresholds and parameters. The system uses the output of curve evaluation to refine future classifications, creating a self-correcting system that improves measurement precision through iterative feedback rather than complex pre-processing corrections.
Solution Approach 2:
The classification model performs self-correction by automatically adapting to different measurement conditions and artifact patterns. Instead of requiring complex external correction algorithms, the system uses its trained understanding of normal versus abnormal qPCR curves to self-correct for baseline drift, thermal noise, and reagent fluctuations inherent in the measurement process.
3Measurement precision
If extensive data sets are used for model training to improve classification accuracy, then measurement precision is improved, but loss of time increases during the training phase
Solution Approach 1:
The patent performs the computationally intensive data collection and model training as a preliminary action before deployment. Once the classification model is trained on extensive datasets, it can rapidly classify new qPCR curves without requiring additional training time. This separates the time-consuming training phase from the rapid evaluation phase, making the loss of training time a one-time investment rather than a recurring penalty.
Solution Approach 2:
The patent creates a trained classification model that serves as a reusable artifact. Instead of retraining on extensive datasets for each new measurement task, the system copies the trained model's knowledge to new evaluation tasks. This allows the extensive training to be performed once, and the resulting model to be rapidly deployed across multiple measurements without repeating the time-consuming training process.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The use of a data-based classification model enhances the accuracy of qPCR curve analysis, reducing misclassifications and improving the reliability of DNA strand segment detection, even in noisy conditions, by distinguishing between presence and nonpresence of DNA strands without relying on typical curve shapes.
Implementation Method 1
at least some of the nucleotides are provided with fluorescent molecules which, upon binding to the individual strand of the DNA strand segment to be detected, activate a fluorescence property
Data Source
AI summary
The disclosure relates to a computer-implemented method for carrying out a quantitative polymerase chain reaction (qPCR) process, comprising the following steps: —cyclically carrying out qPCR cycles; —measuring an intensity value of a fluorescence relating to each qPCR cycle to obtain a qPCR curve from intensity values; —analyzing the shape of the qPCR curve using a data-based classification model trained to provide a classification result depending on the shape of the qPCR curve; and—carrying out the qPCR process depending on the classification result of the analysis of the shape of the qPCR curve.