CNN Genotype Classification for Higher-Specificity Cancer Detection
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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 genotypic data from cell-free DNA (cfDNA) to improve cancer diagnosis.
Innovation Solution
A convolutional neural network (CNN) architecture is trained to classify cancer conditions using genotypic information, where initial filter weights are adjusted based on input vector sets, allowing for the extraction of complex features and improved cancer condition classification.
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 method is simple and noninvasive, but the specificity is low leading to high false-positive results
Solution Approach 1:
The patent replaces traditional mechanical/biochemical assay methods with a computational system based on convolutional neural networks. The CNN architecture processes genotypic data from cfDNA sequencing, substituting complex computational algorithms for simpler traditional biomarker assays, thereby improving specificity while managing complexity through automated machine learning pipelines
Solution Approach 2:
The patent employs a composite approach by combining multiple data sources (genotypic information from cfDNA sequencing) and processing them through a multi-layered CNN architecture. This composite system integrates various computational layers and data processing stages to achieve high specificity in cancer detection, overcoming the limitations of single biomarker approaches
2Measurement precision
If convolutional neural network architecture is used to process genotypic data, then the classification accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the complex task of cancer classification into multiple sequential convolutional layers, each performing specific feature extraction functions. The CNN architecture divides genotypic data processing into discrete computational stages (convolutional layers, pooling layers, fully connected layers), making the overall complex system manageable through modular organization of computational functions
Solution Approach 2:
The patent applies preliminary action by using early convolutional layers to extract and process fundamental features from raw genotypic data before passing them to subsequent layers. The initial layers perform preliminary feature extraction and filtering, preparing simplified representations that reduce the computational burden on later classification layers
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.