Genome Data Refinement for Cancer Mutation Detection
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Solution Overview
Problem
Conventional computer-aided systems for genetic testing face inefficiencies and inaccuracies when processing large volumes of genetic data for cancer analysis, leading to impractical processing delays and reduced effectiveness in identifying mutations associated with cancer.
Innovation Solution
A computing system processes genetic information as text strings, identifying unique segments and mutations, and employs a refinement mechanism to filter out duplicates and noise, reducing the feature set for machine learning models to enhance efficiency and accuracy in cancer diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional computer-aided systems process large volumes of genetic data, then comprehensive cancer analysis is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the large volume of genetic data into smaller manageable units by identifying and processing unique segments and mutations separately. The system divides the genome into specific regions of interest, processes mutations in each region independently, and aggregates results, thereby reducing overall processing time while maintaining comprehensive analysis coverage.
Solution Approach 2:
The system extracts only the essential and relevant features from the genetic data, such as unique segments, mutations, and their locations, rather than processing the entire raw dataset. By taking out only the critical information needed for cancer diagnosis, the system significantly reduces computational burden and processing time while preserving diagnostic accuracy.
2Reliability
If conventional systems analyze all genetic mutations, then comprehensive mutation detection is achieved, but computational complexity and processing overhead increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific regions of the genome that are most relevant to cancer diagnosis. Instead of uniformly analyzing all genetic material, the system identifies and prioritizes unique segments and mutations in cancer-prone regions, allocating higher processing quality to these critical areas while reducing analysis depth in less relevant regions.
Solution Approach 2:
The system performs partial action by analyzing only the necessary subset of mutations required for accurate cancer diagnosis rather than exhaustively processing every possible genetic variant. By identifying and focusing on key nucleotide locations and their associated mutations, the system achieves sufficient diagnostic reliability without the excessive computational complexity of complete genome-wide analysis.
3Measurement precision
If refinement mechanisms filter out all duplicates and noise, then data accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by performing initial filtering and deduplication of genetic data before the main analysis process. The system pre-processes the input data to remove obvious duplicates, normalize formats, and eliminate clear noise patterns, thereby reducing the burden on subsequent refinement stages and improving overall processing efficiency while maintaining data accuracy.
Solution Approach 2:
The refinement mechanism employs self-service by using the structural and contextual information already present in the genetic data to identify and remove duplicates and noise. The system leverages the inherent patterns in DNA sequences, such as known repetitive elements and common artifacts, to automatically filter data without requiring extensive external reference datasets or complex computational models, thus improving efficiency.
Data Source
AI summary
Introduced here is an approach to further refining an initial set of target locations that can serve as inputs to machine learning mechanisms. These target locations may refer to unique molecular positions in a reference human genome and/or mutations thereof that are diagnostically relevant for a given cancer type. The system can implement a refinement mechanism to account for unnecessary or problematic data, such as consecutive/overlapping patterns, non-uniform read counts, insufficient data quality, internal processing noises, and/or insufficient data counts.