Ribosome Data Modeling for Accurate Disease Prediction
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Solution Overview
Problem
Existing drug discovery processes are time-consuming and costly due to the limitations of rule-based approaches, which cannot predict situations beyond human recognition, hindering the efficient prediction of disease presence, type, and prognosis.
Innovation Solution
A disease prediction method using a machine-learning model that constructs a disease prediction model by learning ribosome data and disease information, incorporating data on ribosomal protein expression rates, tissue images, and location relationships to accurately predict disease information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a rule-based approach is used for drug discovery, then the process is simpler to implement, but the prediction accuracy for disease information beyond human recognition is insufficient
Solution Approach 1:
The patent replaces the rule-based mechanical system with a machine learning model that automatically learns patterns from ribosome data. The model substitutes human-defined rules with data-driven predictions, enabling accurate prediction of disease presence, type, and prognosis without requiring pre-programmed medical knowledge.
Solution Approach 2:
The patent transforms the approach by changing from discrete rule-based parameters to continuous machine learning parameters. The model learns complex relationships between ribosome expression data and disease outcomes, transitioning from simple if-then rules to sophisticated pattern recognition that captures non-linear relationships.
2Productivity
If traditional drug discovery methods are used, then the process is more straightforward, but the time and cost required for discovery and development is excessive
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model on extensive ribosome data and disease information before actual drug discovery. This pre-training phase allows the model to learn optimal patterns and relationships, enabling rapid and accurate predictions during the drug discovery process without requiring time-consuming manual analysis.
Solution Approach 2:
The patent creates a computational copy of the biological system through the machine learning model. By training the model to replicate and predict disease states from ribosome data, the system can rapidly simulate and evaluate potential drug effects without physically testing each scenario, significantly reducing time and cost.
3Adaptability or versatility
If human recognition methods are used for disease prediction, then the approach is more interpretable, but the ability to predict situations beyond human recognition is limited
Solution Approach 1:
The patent introduces the machine learning model as an intermediary between raw ribosome data and disease predictions. This intermediary component automatically processes and interprets complex patterns in the data that exceed human cognitive capacity, translating them into actionable predictions about disease presence, type, and prognosis.
Solution Approach 2:
The patent extends prediction capability by adding a new dimension of automated pattern recognition that operates independently of human cognitive limits. The model processes high-dimensional ribosome expression data and identifies complex relationships that would be inaccessible to human analysts, enabling prediction of subtle and rare disease states.
Data Source
AI summary
A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.