Machine Learning Prediction of Human Biological Responses
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Solution Overview
Problem
Conventional candidate antigen testing in vaccines is inefficient and prone to false negatives and false positives due to reliance on animal models that are physiologically distant from humans, leading to the discarding of effective antigens and selection of antigens that perform poorly in humans.
Innovation Solution
A machine learning model trained using molecular sequence data and biological response data from animal and in vitro experiments to predict human and non-human biological responses, leveraging non-linear relationships and reducing dimensionality to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional animal models are used for candidate antigen testing, then pre-clinical evaluation can be performed, but false positives and false negatives increase due to physiological differences from humans
Solution Approach 1:
The patent creates a computational copy of human immune system responses by training machine learning models on human biological response data. This virtual model predicts human immune responses to candidate antigens without requiring physical human testing, thereby eliminating the physiological difference problem between animal and human models while maintaining prediction reliability.
Solution Approach 2:
The patent replaces the mechanical/physical animal testing system with a computational prediction system. Machine learning models process molecular sequence data and predict biological responses algorithmically, substituting the physical biological system (animal models) with an information-processing system that directly models human responses.
2Reliability
If rigorous pre-clinical testing protocols are used, then candidate antigen effectiveness can be evaluated, but testing efficiency decreases and effective antigens may be discarded
Solution Approach 1:
The patent performs preliminary computational screening of candidate antigens using machine learning models before committing to expensive and time-consuming animal studies. The model predicts which candidates are likely to succeed in humans, allowing researchers to prioritize only the most promising candidates for further testing, thereby increasing overall testing efficiency while maintaining reliability.
Solution Approach 2:
The machine learning model serves the dual purpose of both evaluating candidate antigens and learning from outcomes to improve future predictions. The system uses historical data from previous testing cycles to continuously refine its predictions, making the evaluation process increasingly efficient and accurate over time without requiring additional human intervention.
3Loss of information
If animal models are used for vaccine development, then pre-clinical data can be obtained, but the data may not accurately predict human responses leading to false conclusions
Solution Approach 1:
The patent changes the fundamental parameter of what system is being tested - from animal physiological responses to direct human immune response modeling. By training the machine learning model exclusively on human biological response data, the system predicts human-specific parameters (human antibody responses, human immune cell reactions) rather than attempting to translate from animal responses, thereby eliminating information loss due to species differences.
Data Source
AI summary
Systems and methods can apply machine learning techniques to predict biological responses. One of the methods is performed by at least one processor executing executable logic including at least one machine learning model trained to predict biological responses. The method includes receiving first sequence data of a first molecular sequence, receiving second sequence data of a second molecular sequence, and predicting a biological response for the second molecular sequence based at least partly on the received first and second sequence data.