AI Biomarker Screening Using Encoded Biodata Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing biomarker selection methods are time-consuming and ineffective for diseases like cardiovascular diseases, limiting the development of companion diagnostics and treatment options, particularly for existing companion technologies, and existing methods fail to address the need for specific biomarkers in the context of cardiovascular diseases, particularly in the context of cardiovascular diseases, where companion diagnostics are absent or inadequate.
Innovation Solution
An artificial intelligence-based biomarker selection device and method that utilizes an encoder to calculate a numeric vector from biodata and a biomarker screening unit to identify biomarkers, leveraging supervised and self-supervised learning to find matching and non-matching pairs in a common space, and employing neural networks to screen biomarkers effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional biomarker selection methods (PCR, ISH, NGS, IHC) are used, then existing companion diagnostics can be developed, but the process is very difficult and time-consuming, and no effective biomarkers exist for diseases like cardiovascular diseases
Solution Approach 1:
The patent replaces traditional mechanical laboratory methods (PCR, ISH, NGS, IHC) with an AI-based computational system that uses neural networks to automatically select biomarkers from electronic health records and imaging data, dramatically reducing the time and complexity of the selection process
Solution Approach 2:
The AI system performs self-learning and automated biomarker selection without requiring manual intervention in the complex laboratory procedures, allowing the system to independently process patient data, identify patterns, and select appropriate biomarkers based on trained models
2Adaptability or versatility
If traditional biomarker selection methods are used, then established diseases like cancer can be addressed, but there are no biomarkers for other diseases such as cardiovascular diseases
Solution Approach 1:
The patent creates a universal AI-based biomarker selection system that can be applied across multiple disease types including cardiovascular diseases, cancer, and other conditions, allowing the same platform to serve diverse medical needs without requiring disease-specific separate systems
Solution Approach 2:
The system adapts to different diseases by changing the input data parameters and training models accordingly, allowing the same AI framework to effectively identify biomarkers for cardiovascular diseases, cancer, and other conditions by adjusting to disease-specific characteristics
Data Source
AI summary
The embodiments relate to an artificial intelligence-based biomarker selection device and method, the artificial intelligence-based biomarker selection device comprising: an acquisition unit for acquiring biodata; an encoder for receiving the biodata and calculating a numeric vector including one or more elements which are biomarker candidates; and a biomarker screening unit for screening biomarkers from the one or more elements of the numeric vector. The device and method of the embodiments may be used for discovering various biomarkers usable for drug discovery, such as the development of, for example, a new cardiovascular drug, or for prognosis prediction. Accordingly, the device and method may be effectively used for a pharmaceutical platform for new drug development, or for a research platform for precision medicine, disease diagnosis, treatment optimization, etc.