CNN Genotypic Data Classification for Specific Cancer Diagnosis
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Solution Overview
Problem
Existing noninvasive biomarkers for cancer detection, such as CA 125, CA19-9, and PSA, have low specificity, leading to high false-positive results, and there is a need for more accurate processing of circulating cell-free DNA (cfDNA) data to derive robust classifiers for cancer diagnosis.
Innovation Solution
A convolutional neural network (CNN) architecture is trained using genotypic data constructs formatted into vector sets, adjusting filter weights to classify cancer conditions based on genotypic information from biological samples, including cfDNA, to improve diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noninvasive biomarkers (CA 125, CA19-9, PSA) are used for cancer detection, then the detection process is simple and noninvasive, but the specificity is low leading to high false-positive results
Solution Approach 1:
The patent segments the cfDNA data processing into multiple convolutional neural network layers, each performing specific feature extraction and classification tasks. This segmentation allows the system to achieve high specificity by progressively analyzing different aspects of the genotypic data rather than relying on a single simple biomarker test.
Solution Approach 2:
The patent introduces a convolutional neural network as an intermediary between the raw cfDNA data and the final cancer classification. This intermediary processes the complex genotypic information through multiple layers of filtering and feature extraction, transforming it into reliable diagnostic classifications with high specificity.
2Measurement precision
If convolutional neural network architecture is used to process cfDNA data, then the classification accuracy and specificity are improved, but the computational complexity increases
Solution Approach 1:
The CNN architecture is segmented into multiple specialized layers (convolutional layers, pooling layers, fully connected layers) that perform distinct computational tasks. This segmentation enables the system to achieve high classification accuracy by breaking down the complex analysis into manageable stages, processing cfDNA data progressively rather than requiring all computations simultaneously.
Solution Approach 2:
The patent transforms the one-dimensional cfDNA fragment size data into two-dimensional feature maps through convolutional operations. This dimensional transformation allows the application of image processing techniques to genomic data, enabling more sophisticated pattern recognition and significantly improving classification accuracy while maintaining computational feasibility through efficient convolution operations.
3Reliability
If multiple convolutional layers with filter weights are used to analyze genotypic data, then the ability to identify complex cancer patterns is enhanced, but the training time and computational resources required increase
Solution Approach 1:
The patent performs preliminary action by pre-training the convolutional neural network on large datasets of genotypic information before deployment. During this offline training phase, the filter weights are optimized to recognize complex cancer patterns. Once trained, the model can rapidly classify new samples with high reliability without requiring extensive computation time during actual diagnostic use.
Solution Approach 2:
The patent implements feedback mechanisms during the training process where the network's classification outputs are compared against known cancer diagnoses, and the filter weights are adjusted accordingly through backpropagation. This iterative feedback process enhances the robustness of the classifier by continuously improving its ability to recognize complex cancer patterns based on performance feedback.
Data Source
AI summary
Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.