Frugal Biomarker Selection for Accurate Biological Sample Classification
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Solution Overview
Problem
Existing biomarker identification methods rely on a large number of features, leading to high costs and inefficiencies in clinical deployment, while methods using fewer features lack accuracy, and there is a need for a cost-effective and accurate diagnostic method that can guide therapeutic design.
Innovation Solution
A method and system for identifying a frugal set of biomarkers using a small subset of features, employing a combination of microbial taxonomic groups and ensemble classification models, along with multiplex qPCR to accurately classify biological samples and guide therapeutic interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of biomarkers are used for disease diagnosis, then diagnostic accuracy is improved, but measurement cost and system complexity increase
Solution Approach 1:
The patent extracts and selects only the most critical biomarkers from a comprehensive panel. By using machine learning algorithms to identify and extract the essential features (biomarkers) that provide maximum diagnostic information, the system reduces the number of measurements required while maintaining high accuracy. This extraction principle allows the system to focus on a subset of biomarkers that are most informative for the specific disease state being diagnosed.
Solution Approach 2:
The patent changes the approach from measuring all possible biomarkers to measuring a optimized subset. By using machine learning models to determine which biomarkers are most predictive, the system dynamically adjusts the measurement parameters to focus on the most relevant biomarkers. This parameter change enables the system to achieve high diagnostic accuracy with fewer measurements, thereby reducing cost and complexity.
2Measurement precision
If a large number of biomarkers are measured through high-throughput screening, then diagnostic accuracy is improved, but cost and time for clinical deployment increase
Solution Approach 1:
The patent performs preliminary action by pre-identifying and pre-selecting the most relevant biomarkers using machine learning algorithms trained on comprehensive datasets. This preliminary selection of essential biomarkers is done before actual diagnostic testing, allowing the system to be deployed more quickly. The machine learning model is pre-trained to recognize patterns in a reduced set of biomarkers, eliminating the need for time-consuming analysis of large biomarker panels during clinical deployment.
Solution Approach 2:
The system extracts only the most time-critical and informative biomarkers for measurement. By using machine learning to identify which biomarkers provide the most diagnostic value, the system can implement a streamlined measurement protocol that reduces testing time while maintaining accuracy. This extraction of essential features enables faster clinical deployment compared to comprehensive high-throughput screening of all possible biomarkers.
3Device complexity
If a small subset of biomarkers is used for diagnosis, then measurement cost is reduced, but diagnostic accuracy deteriorates
Solution Approach 1:
The patent changes the measurement parameters by selecting and optimizing a specific subset of biomarkers. Through machine learning algorithms, the system identifies which biomarkers, when measured together, provide the maximum diagnostic information. This parameter optimization ensures that even though fewer biomarkers are measured, the diagnostic accuracy is maintained or improved through the strategic selection of the most informative markers.
Solution Approach 2:
The patent creates a composite diagnostic approach by combining a limited number of carefully selected biomarkers with machine learning classification models. This composite approach integrates multiple data types and analytical methods to achieve high diagnostic accuracy. The machine learning model processes the combined information from the selected biomarkers to make accurate diagnostic decisions, compensating for the reduced number of individual measurements through sophisticated data integration and pattern recognition.
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
Enables accurate disease diagnosis and personalized therapeutic design by using a small number of biomarkers, reducing measurement costs and facilitating causal analysis.
Implementation Method 1
a minimum number of multiplexed qPCR runs required for determining the relative abundance of each of the microbial taxonomic groups constituting the frugal set of markers are performed
Data Source
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AI summary
The present disclosure is related to method and system for identifying and utilizing frugal markers for classification of biological sample. Discovering an optimal and/ or frugal set of features/ biomarkers form a large set of features measured through high-throughput screening techniques, which can characterize a disease/ anomaly with sufficient accuracy, still remains a challenge. According to the present disclosure, given a set of measurements of multiple features characterizing biological samples obtained from disease cases and healthy controls, a classification model combining the measured values of a small subset of the features is computed. The classification model is then used for classifying between disease cases and healthy controls.