Genomic Feature Clustering for Disease Subtype Classification
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Solution Overview
Problem
Current methods for determining prognostic classifications of diseases, particularly cancer, are time-consuming, costly, and often inaccurate, relying on manual genetic and histological analyses that can delay diagnosis and treatment.
Innovation Solution
The use of predictive models that analyze genomic features derived from DNA and RNA sequencing data to classify diseases and subtypes through clustering techniques, enabling rapid and accurate prognostic classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual genetic and histological analysis is used to determine prognostic classification, then comprehensive diagnostic evaluation can be performed, but the process becomes time-consuming and costly
Solution Approach 1:
The patent extracts and isolates specific genomic features and genetic markers from comprehensive genetic analysis that are most predictive of prognosis. By focusing on a curated set of key genomic features rather than analyzing all genetic data manually, the system achieves accurate prognostic classification while significantly reducing the time required for diagnosis.
Solution Approach 2:
The patent replaces manual genetic and histological analysis with an automated computational system that uses machine learning algorithms to analyze genomic features. This substitution of mechanical/manual processes with automated computational methods enables rapid processing of genetic data while maintaining or improving classification accuracy.
2Measurement precision
If comprehensive genetic analysis and complex weighting of data points is performed to achieve accurate subtype classification, then diagnostic accuracy is improved, but the process becomes costly and complex
Solution Approach 1:
The patent segments the complex diagnostic process into distinct modules: genomic feature extraction, feature selection, clustering analysis, and prognostic classification. Each module handles a specific aspect of the analysis, making the overall complex process more manageable and interpretable while maintaining accuracy through systematic processing of genetic data.
Solution Approach 2:
The patent changes the parameters of analysis by focusing on specific genomic features and genetic markers that have been identified as most predictive of prognosis. By transforming the diagnostic approach to prioritize key genomic parameters rather than attempting to weigh all available data points equally, the system achieves accurate classification with reduced complexity.
3Reliability
If traditional manual analysis methods are used for prognostic classification, then thorough evaluation can be performed, but treatment decisions are delayed
Solution Approach 1:
The patent performs preliminary action by pre-identifying and pre-processing genomic features that are most relevant for prognostic classification. The system prepares and organizes genetic data in advance, extracting key features before the actual diagnostic decision is needed. This preliminary processing enables rapid and reliable prognostic evaluation when clinical decisions must be made urgently.
Data Source
AI summary
Techniques for performing prognostic classifications using unsupervised clustering are described. An example method includes determining features of a sample from a subject. The features, for instance, include an MMRD probability score of the sample and/or a copy number state of at least one genetic loci based on nucleic acid molecules of the sample. Input data is generated indicating the features. The example method further includes determining that the input data corresponds to at least one cluster in the clustering model and determining a prognostic classification of the subject based on the at least one cluster.