Blood RNA Transcript Panels for Non-Invasive Neurodegenerative Diagnosis
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Solution Overview
Problem
Current methods for diagnosing neurodegenerative diseases are expensive, time-consuming, or invasive, leading to a need for a simple, inexpensive, and non-invasive diagnostic approach.
Innovation Solution
A computer-implemented method using machine learning to analyze blood RNA transcripts, employing a Random Forest algorithm to identify a small set of transcripts that can distinguish neurodegenerative diseases with high sensitivity and specificity, leveraging publicly available datasets and machine learning techniques to select and validate blood-based RNA biomarkers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic methods are used, then diagnostic accuracy can be achieved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent extracts and analyzes specific RNA transcripts from whole blood samples to create a diagnostic signature. By focusing on a selected subset of RNA transcripts rather than comprehensive testing, the method achieves accurate neurodegenerative disease diagnosis while reducing time and resource requirements compared to traditional diagnostic approaches
Solution Approach 2:
The patent replaces traditional mechanical and invasive diagnostic procedures (such as lumbar punctures or brain imaging) with a computational approach using machine learning algorithms that analyze RNA expression patterns from simple blood samples, thereby reducing time consumption and procedural complexity while maintaining diagnostic accuracy
2Measurement precision
If current diagnostic methods are used, then diagnostic accuracy can be achieved, but the invasiveness increases
Solution Approach 1:
The patent extracts diagnostic information from easily obtainable whole blood samples rather than requiring invasive procedures. By identifying and analyzing specific RNA transcripts present in blood, the method achieves neurodegenerative disease diagnosis without the harm and discomfort associated with invasive testing
Solution Approach 2:
The patent uses whole blood as an intermediary medium to detect neurodegenerative disease. The RNA transcripts in blood serve as mediators that reflect brain pathology, allowing non-invasive diagnosis while maintaining accuracy by analyzing molecular signatures that connect peripheral blood to central nervous system conditions
3Measurement precision
If comprehensive transcript analysis is performed, then diagnostic accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex transcriptome into specific, predetermined RNA transcripts that are most relevant for neurodegenerative disease diagnosis. By focusing on a curated subset of transcripts rather than analyzing all transcripts, the method maintains high diagnostic accuracy while significantly reducing analytical complexity and computational requirements
Solution Approach 2:
The patent performs preliminary identification and validation of specific RNA transcripts that serve as reliable biomarkers for neurodegenerative diseases. This pre-selection of informative transcripts simplifies the subsequent diagnostic analysis by establishing a fixed panel of markers to be measured and analyzed, reducing complexity while preserving diagnostic power
Data Source
AI summary
A processor is configured to implement a machine learning model that is trained to select transcripts in blood for distinguishing neurodegenerative diseases. The algorithm is developed via machine learning and leverages concepts associated with blood-based changes in mRNA gene expression for differentiating patients of any neurodegenerative disease regardless of the proteins or their post-translational modifications occurring in disease.