Machine Learning Nucleic Acid Detection via Amplification Curve Analysis
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Solution Overview
Problem
Current multiplex nucleic acid detection methods are limited by their reliance on costly equipment, low throughput, and the need for extensive optimization, particularly in distinguishing between multiple target nucleic acids using fluorescent probes and post-amplification processing.
Innovation Solution
A computer-implemented method utilizing machine learning models to process amplification and melting curve data from real-time PCR or digital PCR, enabling the identification of multiple target nucleic acids in a single reaction with increased accuracy and scalability, without the need for large laboratory equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescent probes and post-amplification processing are used for multiplex nucleic acid detection, then detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and utilizes only the amplification curve data from real-time PCR or digital PCR reactions, eliminating the need for fluorescent probes and post-amplification processing steps. The machine learning model processes the inherent amplification curve characteristics to distinguish multiple targets, removing complex additional components while maintaining detection accuracy.
Solution Approach 2:
The patent makes the amplification curve serve multiple functions: it simultaneously provides quantitative information about target presence and qualitative information for target identification through machine learning analysis. This multi-functional use of the amplification curve eliminates the need for separate fluorescent probe systems and post-amplification processing methods.
2Productivity
If traditional multiplexing methods are used, then multiple nucleic acids can be detected, but throughput is limited and optimization is extensive
Solution Approach 1:
The patent performs preliminary training of the machine learning model using labeled amplification curve data from multiple targets before actual detection. This pre-training phase establishes the model's ability to distinguish different targets, eliminating the need for extensive optimization during each new multiplexing experiment and enabling rapid deployment for high-throughput diagnostics.
Solution Approach 2:
The patent changes the analytical parameters by using the entire amplification curve shape and characteristics as input features for the machine learning model, rather than relying on traditional endpoint measurements or requiring separate optimization parameters for each target. This parameter transformation enables simultaneous detection of multiple targets without extensive method optimization.
3Adaptability or versatility
If qPCR with multidimensional standard curves is used, then multiplexing is enabled, but data volume is limited and reliability is reduced
Solution Approach 1:
The patent transitions from traditional two-dimensional endpoint analysis to utilizing the entire time-series amplification curve as a high-dimensional dataset. The machine learning model processes multiple features from the amplification curve shape, kinetics, and progression, effectively adding dimensional information that enhances both multiplexing capability and reliability of target identification.
Data Source
AI summary
Disclosed herein is a computer-implemented method of identifying the presence of any of a plurality of prospective target nucleic acids in a solution containing a biological sample. The method comprises receiving amplification curve data indicative of an amplification reaction associated with at least one unknown nucleic acid present in the solution; processing the received data, wherein the processing comprises inputting input data into a machine learning model trained to identify any of the plurality of prospective target nucleic acids, wherein the input data is based on the amplification curve data and is indicative of the degree of amplification of the at least one unknown nucleic acid over time during the amplification reaction; and based on the processing, determining that the at least one unknown nucleic acid is one of the plurality of prospective nucleic acids, and thereby identifying the presence of at least one of the plurality of target nucleic acids in the solution.