Dimension Extraction for Protein Structural Analysis
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Solution Overview
Problem
Current methods for structural analysis of proteins, such as MD simulations, face challenges in selecting appropriate reaction coordinates, leading to incomplete extraction of structural changes due to the lack of effective methods for identifying unknown relevant dimensions.
Innovation Solution
An information processing apparatus that performs clustering using candidate dimensions to determine hidden dimensions, which are then added to the analysis group to maximize the number of clusters generated, enabling more detailed and precise structural analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clustering is performed using only known relevant dimensions, then the analysis can be kept simple and computationally efficient, but unknown relevant dimensions cannot be identified leading to incomplete structural analysis
Solution Approach 1:
The patent applies dimensionality change by testing candidate dimensions one by one and adding them to the dimension group sequentially. For each candidate dimension, clustering is performed in an expanded dimensional space, and if the number of clusters increases significantly, that dimension is added to the dimension group. This systematic exploration of additional dimensions enables discovery of unknown relevant dimensions while maintaining manageable computational complexity through incremental expansion.
Solution Approach 2:
The patent performs preliminary clustering analysis using known relevant dimensions before introducing candidate dimensions. This preliminary action establishes a baseline clustering result that can be compared against results obtained when candidate dimensions are added. By having this reference point established in advance, the method can efficiently evaluate whether new dimensions provide meaningful additional information without redundant computation.
2Measurement precision
If all candidate dimensions are tested and added to improve analysis completeness, then all relevant dimensions can be identified, but the computational cost and time increase significantly
Solution Approach 1:
The patent implements partial action by testing candidate dimensions in a limited sequence rather than exhaustively evaluating all possible dimensions simultaneously. The method adds dimensions to the dimension group only when they produce a significant increase in cluster number, stopping the expansion when no further meaningful dimensions are found or when a predetermined limit is reached. This approach achieves sufficient analytical completeness without the prohibitive computational cost of exhaustive dimension exploration.
Solution Approach 2:
The patent segments the dimension evaluation process into discrete steps where individual candidate dimensions are tested and evaluated separately. Each candidate dimension is assessed independently by performing clustering with that dimension added to the current dimension group, and decisions are made incrementally. This segmentation allows for efficient resource allocation and early termination when additional dimensions no longer provide benefit, reducing overall computational time.
3Reliability
If multiple candidate dimensions are evaluated and added to the dimension group, then the structural analysis becomes more accurate, but the device complexity and processing requirements increase
Solution Approach 1:
The patent applies dynamics by making the dimension group flexible and adaptive rather than fixed. The dimension group starts with known relevant dimensions and dynamically expands by incorporating candidate dimensions that demonstrate value through clustering analysis. This dynamic adjustment allows the system to adapt its complexity level based on the specific analysis needs and data characteristics, achieving high reliability only when necessary while avoiding unnecessary complexity.
Solution Approach 2:
The patent implements feedback mechanisms by evaluating the impact of each candidate dimension on clustering results before committing to its inclusion. The number of clusters generated serves as feedback to determine whether a candidate dimension should be added to the dimension group. This feedback-driven approach ensures that each added dimension contributes meaningfully to analysis reliability, preventing unnecessary complexity from being introduced into the system.
Data Source
AI summary
An information processing apparatus includes: a memory configured to store a plurality of structures of a substance whose structure changes and a dimension group, which is a group of index dimensions that are indices for structural analysis of the substance, out of a plurality of dimensions that express the structure of the substance; and a processor configured to perform a procedure including: performing, for each of a plurality of candidate dimensions, which are not included in the dimension group, out of the plurality of dimensions, clustering of a plurality of structures in multidimensional space that has every index dimension included in the dimension group and a candidate dimension as coordinate axes; and adding a specified candidate dimension for which it is possible to generate a largest number of clusters to the dimension group as an index dimension.