Sequence Read Transformation for Accurate Condition Classification
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Solution Overview
Problem
Existing genomic sequencing methodologies, such as WGS and WES, generate substantial data volumes that are difficult to process accurately for identifying health conditions like cancer, often requiring extensive resources and failing to detect certain conditions directly.
Innovation Solution
Transforming sequence read data into alternate domains, like frequency or wavelet domains, to reveal predictive features for health conditions, using predictive models to determine conditions like cancer types, stages, or general health, and reporting results to care providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If extensive genomic sequencing methodologies (WGS, WES) are used to obtain comprehensive genetic data, then the context and information about health conditions are improved, but the data processing complexity and computational resources required increase substantially
Solution Approach 1:
The patent extracts only the essential genetic features and characteristics from the comprehensive sequencing data that are most relevant to predicting health conditions. By selecting and extracting key genetic markers and patterns rather than processing all raw sequence data, the system maintains high predictive accuracy while substantially reducing computational complexity and resource requirements.
Solution Approach 2:
The patent performs preliminary processing and transformation of genomic data into structured formats before analysis. By pre-processing the data to identify and organize key genetic features in advance, the system reduces the complexity of subsequent analysis steps while preserving the essential information needed for accurate health condition prediction.
2Speed
If direct evaluation of sequence read data is performed to identify health conditions, then the processing speed is maintained, but the accuracy of condition identification decreases for certain conditions
Solution Approach 1:
The patent introduces an intermediary processing step that transforms raw sequence read data into a different representation format that reveals patterns not apparent in the original data. This intermediary transformation layer enables more accurate identification of certain health conditions while maintaining efficient processing speeds, as the transformed data exposes predictive features that are otherwise hidden.
Solution Approach 2:
The patent changes the parameters or representation of the genetic data through mathematical transformations. By converting the data into alternate domains or representations, the system reveals new patterns and features that improve condition identification accuracy without requiring substantially more processing time, as the transformation is computationally efficient.
3Measurement precision
If substantial processing resources are allocated to analyze sequence read data, then the accuracy of condition identification improves, but the processing time and computational cost increase
Solution Approach 1:
The patent extracts and focuses computational resources on analyzing only the most predictive genetic features and markers rather than processing all sequence data equally. By identifying and concentrating analysis on key discriminative features, the system achieves high condition identification accuracy while substantially reducing the total processing time and computational resources required.
Solution Approach 2:
The patent performs preliminary filtering and prioritization of genetic data to identify the most relevant features before detailed analysis. By pre-sorting and prioritizing data based on predictive importance, the system ensures that computational resources are allocated efficiently to the most informative data points, achieving high accuracy without excessive processing time.
Data Source
AI summary
Techniques for identifying conditions of a subject are described. An example method includes identifying sequence read data of a sample obtained from a subject. The sequence read data is in a spatial domain corresponding to genomic position. The example method further includes generating transformed data by transforming the sequence read data into an alternative domain; generating input features based on the transformed data; and classifying, using a classifier, a condition of the subject based on the input features.