Gene Dependency Estimation Using Clustered Time-Series Analysis
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Solution Overview
Problem
Existing methods struggle to determine dependencies among multiple biological materials effectively when the number of measurement time points is less than the number of genes, leading to indeterminate estimation results due to similar temporal variations among genes.
Innovation Solution
A biological material analysis method that groups biological materials based on temporal variations and biological functions, generating representative time-series data to estimate dependencies between groups, using techniques like Bayesian networks and gene ontology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of measurement time points is increased to improve dependency estimation accuracy, then the reliability of estimation results is improved, but the cost and effort increase significantly
Solution Approach 1:
The patent segments the large set of genes into multiple clusters based on temporal variation patterns. By dividing genes into clusters with similar expression patterns, the method reduces the effective number of independent variables that need to be analyzed, enabling reliable dependency estimation even with limited time points.
Solution Approach 2:
The patent creates representative time-series data that captures the essential temporal variation patterns of gene clusters. These representative patterns serve as proxies for individual genes within clusters, reducing the dimensionality of the problem while preserving the key information needed for dependency estimation.
2Productivity
If the number of genes analyzed is increased to improve comprehensiveness, then the productivity of biological analysis is improved, but the difficulty of determining estimation results increases when time points are limited
Solution Approach 1:
The patent divides the comprehensive set of genes into multiple clusters based on temporal variation similarities. This segmentation allows the analysis to handle a large number of genes by processing them in manageable clusters, reducing the computational and statistical difficulty while maintaining comprehensive coverage.
Solution Approach 2:
The patent merges genes with similar temporal variation patterns into the same cluster and generates a single representative time-series for each cluster. This merging reduces the number of independent variables from thousands of individual genes to a manageable number of cluster representatives, making dependency estimation feasible.
3Ease of operation
If genes are grouped only by temporal variation similarity, then the ease of operation is improved, but the loss of biological functional information increases
Solution Approach 1:
The patent applies different quality criteria to different aspects of gene grouping. Temporal variation similarity is used as the primary criterion for clustering genes into groups, while biological functional information is preserved and integrated separately when estimating dependencies between clusters, ensuring both operational simplicity and biological relevance.
Solution Approach 2:
The patent uses representative time-series data as an intermediary between individual gene expressions and cluster-level dependency analysis. This intermediary preserves the temporal variation information needed for grouping while allowing integration of biological functional context during dependency estimation without requiring direct manipulation of individual gene data.
Data Source
AI summary
Time-series data formed by acquiring values indicating amounts or states of a plurality of biological materials for each biological material at a plurality of time points is prepared, the plurality of biological materials are divided into a plurality of groups on the basis of temporal variations of time-series data of the respective biological materials, representative time-series data indicating a state of each group is generated on the basis of time-series data of at least one biological material included in each group, and dependencies between the groups are estimated on the basis of the representative time-series data of each group.