Gene Prediction Device Using Function Information Segmentation
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Solution Overview
Problem
Existing prediction devices are not accurate due to errors in model information used for simulating analysis targets, particularly as they do not account for differences in gene sequences and environmental conditions of living organisms.
Innovation Solution
A prediction device and method that generate function information for a gene sequence based on model information representing the relevance between sequence information and function information, and use this to predict observation information by incorporating environment information and observed data, thereby improving the accuracy of simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general model information is used for simulation, then the prediction process is simple, but the prediction accuracy is low due to errors and lack of specificity
Solution Approach 1:
The patent divides the model processing into two distinct stages: first model processing that generates function information from sequence information, and second model processing that generates prediction information from function information and environment information. This segmentation allows each stage to focus on specific aspects, improving overall prediction accuracy while maintaining manageable complexity through modular processing.
Solution Approach 2:
The patent introduces function information as an intermediary between sequence information and observation information. Function information serves as a mediator that captures the functional characteristics of gene sequences, enabling more accurate predictions without directly complexifying the relationship between genetic data and observed traits. This intermediary layer simplifies the overall modeling approach while improving precision.
2Measurement precision
If model information does not account for gene sequence differences, then the model is simpler, but the prediction accuracy deteriorates
Solution Approach 1:
The patent applies local quality by generating function information specifically tailored to each gene sequence's unique characteristics. Instead of using a one-size-fits-all model, the system creates sequence-specific function information that captures local functional variations, enabling accurate predictions for diverse organisms while maintaining a unified processing framework.
Solution Approach 2:
The patent changes the parameters of the model by introducing function information as a new parameter that mediates between sequence information and environment information. This parameter transformation allows the model to adapt to different gene sequences and environmental conditions without requiring complete model redesign, thus improving accuracy while maintaining versatility.
3Measurement precision
If function information is generated for each gene sequence, then prediction accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing sequence information to generate function information before the main prediction process. This preliminary generation of function information allows the second model processing stage to focus solely on combining function information with environment information, reducing the computational burden during actual prediction and overall processing time.
Solution Approach 2:
By segmenting the processing into two distinct stages, the patent enables parallelization and optimization of each stage independently. The first stage can pre-compute function information for multiple sequences, while the second stage efficiently combines this pre-computed information with environmental data, reducing total processing time while maintaining high accuracy.
Data Source
AI summary
Provided are a prediction device and the like capable of more accurately simulating an analysis target. The prediction device generates function information for a gene sequence of a living body to be an analysis target based on first model information representing a relevance between sequence information and the function information, the sequence information representing the gene sequence of the analysis target, the function information representing a function potentially expressed by the gene sequence; and generates prediction information representing observation information predicted for the analysis target based on second model information and the function information, the second model information representing a relevance among the function information of the living body, environment information representing an environment around the living body, and the observation information observed for the living body, the function information being generated for the gene sequence of the analysis target.