Spatial Representation of Biological Data for Classification
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Solution Overview
Problem
The analysis of large biological datasets is challenging due to their size and the gaps in existing knowledge, which complicates or obstructs effective data analysis.
Innovation Solution
A computer system that transforms biological data into a spatial representation, selectively normalizes it, and converts it into an output image for analysis using image-analysis techniques, such as pretrained neural networks, to determine classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biological datasets are analyzed directly in their original form, then comprehensive analysis can be performed, but computational complexity and analysis time increase significantly
Solution Approach 1:
The patent transforms biological data by changing its representation parameters from raw sequential format to spatial image format, and applies selective normalization to adjust intensity ranges. This parameter transformation enables the use of efficient image analysis algorithms while preserving the essential information needed for accurate classification, thereby reducing analysis time without sacrificing classification accuracy.
Solution Approach 2:
The patent creates a visual copy of the biological data in the form of an image representation. This copy captures the essential patterns and relationships in the data while allowing parallel processing and optimization techniques from image analysis to be applied, significantly accelerating the analysis process compared to direct processing of the original biological data format.
2Measurement precision
If traditional data analysis methods are used on large biological datasets, then thorough analysis is possible, but computational resources and costs increase
Solution Approach 1:
The patent substitutes traditional mechanical data processing methods with optical-inspired image analysis techniques. By transforming biological data into visual representations, the system can leverage highly optimized image processing hardware and algorithms that are more energy-efficient than conventional data analysis methods, reducing computational costs while maintaining classification accuracy.
3Stability of the object's composition
If complete normalization is applied to all data ranges, then data consistency is improved, but information from high-intensity regions is lost
Solution Approach 1:
The patent applies selective normalization that treats different intensity regions differently. Low-intensity regions are normalized to a full range to enhance visibility and consistency, while high-intensity regions are preserved with their original dynamic range to retain important information. This local differentiation strategy maintains data consistency where needed while preserving critical high-intensity information.
Data Source
AI summary
A computer that analyzes data is described. During operation, the computer may access the data in the memory. Then, the computer may transform the data into a spatial representation. For example, for biological data, the transformation may be based at least in part on a predefined relationship between the biological data and corresponding spatial locations in a genome. Moreover, the computer may selectively normalize the transformed data to obtain normalized transformed data. Notably, the selective normalization may use different normalization ranges based at least in part on expression levels in a type of biological sequencing. Next, the processor may convert the normalized transformed data into an output image. Furthermore, the processor may analyze the image using an image-analysis technique (such as a pretrained neural network) to determine a classification. Additionally, the processor may perform: storing the classification; displaying the classification; and/or providing the classification to an electronic device.