Genotypic Causal Model for Genetic Data Analysis
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Solution Overview
Problem
The vast amount of available data related to human physiology, particularly genetic sequencing, poses a challenge in discovering root cause information for disease conditions due to the sheer quantity of data and resulting combinatorial explosions in possible correlations, making it difficult to identify effective correlations between genetic data and disease states.
Innovation Solution
A system and method that utilize feature learning algorithms to generate a genotypic causal model by creating a causal graph with genotypic and symptomatic nodes, identifying correlated gene combinations and symptoms with disease states, and connecting these nodes to establish causal relationships, enabling the identification of paths from genetic sequences to symptomatic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If genetic sequencing data is collected from increasingly large populations, then the quantity of genetic data increases, but the difficulty of analyzing and discovering root cause information increases due to combinatorial explosion
Solution Approach 1:
The patent segments the complex analysis task into two distinct feature learning algorithms: a first feature learning algorithm that processes genetic sequencing data to identify genotypic features, and a second feature learning algorithm that processes symptomatic data to identify phenotypic features. This segmentation divides the combinatorial explosion problem into two smaller, more manageable analysis tasks that can be processed independently and then integrated.
Solution Approach 2:
The patent introduces a causal model as an intermediary structure that connects genotypic features from the first algorithm with phenotypic features from the second algorithm. This causal model serves as a mediator that organizes the relationship between genetic data and disease states, making the overall system more manageable by providing a structured framework for integration.
2Measurement precision
If feature learning algorithms are used to identify correlations, then the ability to discover root cause information improves, but the computational complexity increases
Solution Approach 1:
The computational task is segmented into two separate feature learning algorithms rather than one comprehensive algorithm. The first algorithm focuses specifically on learning features from genetic sequencing data, while the second algorithm focuses on learning features from symptomatic data. This segmentation reduces the computational complexity by dividing the feature space into two distinct domains that can be processed independently.
Solution Approach 2:
The patent extracts and separates the feature learning process into distinct components: genotypic feature extraction from genetic data and phenotypic feature extraction from symptomatic data. By extracting these features separately through dedicated algorithms, the system avoids the computational burden of learning all features simultaneously from combined data sources.
Data Source
AI summary
A system for generating a genotypic causal model of a disease state includes a computing device that generates a causal graph containing genotypic causal nodes and connected symptomatic causal nodes, which contains causal paths from gene combinations to symptomatic datums. Genotypic causal nodes and/or connected symptomatic causal nodes may be generated by feature learning algorithms from training data.