Machine Learning Algorithms for Biological Data Semantic Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The processing of vast amounts of biology-related data is time-consuming and expensive due to the need for manual analysis, which hinders efficient analysis and classification of biological images and language-based data.
Innovation Solution
A system utilizing machine-learning algorithms, specifically language recognition and visual recognition algorithms, to generate high-dimensional representations of biology-related data, allowing for semantic mapping and improved classification of images and text data, enabling accurate search and tagging of biological images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to process biology-related data, then analysis accuracy can be maintained through human expertise, but processing time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis with automated machine learning algorithms. The system uses trained ML models to process biological images and data, substituting human expert analysis with computational algorithms that can operate at scale without time constraints, while maintaining consistent accuracy through standardized processing pipelines.
Solution Approach 2:
The system enables self-service processing where the machine learning algorithms autonomously analyze biological data without requiring continuous human intervention. The trained models automatically process images, generate classifications, and produce results independently, freeing researchers from time-consuming manual analysis tasks.
2Speed
If traditional image classification methods are used, then processing speed can be maintained, but classification accuracy deteriorates due to inability to capture semantic relationships
Solution Approach 1:
The patent transforms image data into high-dimensional feature spaces using machine learning algorithms. By mapping images to high-dimensional representations, the system captures complex semantic relationships and patterns that traditional 2D image processing cannot detect, significantly improving classification accuracy while maintaining processing speed through optimized computational pipelines.
Solution Approach 2:
The system changes the parameter space by transforming images from pixel space to feature space through machine learning transformations. This parameter transformation allows the system to capture abstract semantic features and relationships, enabling accurate classification of biological images while maintaining computational efficiency.
3Device complexity
If one-hot encoding is used for data representation, then computational simplicity is maintained, but semantic similarity between different inputs cannot be captured
Solution Approach 1:
The patent changes the representation parameters from discrete one-hot encoding to continuous high-dimensional vectors. This transformation allows semantic information to be preserved and relationships between different biological inputs to be captured through vector similarity, while the computational complexity remains manageable through efficient vector operations.
Solution Approach 2:
The system creates composite representations by combining multiple features and attributes into unified high-dimensional vectors. These composite vectors encapsulate diverse semantic information about biological images and data, enabling the system to capture nuanced relationships while maintaining computational tractability through standardized vector operations.
4Measurement precision
If extensive manual annotation is performed to improve training data quality, then model accuracy can be improved, but time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary actions by using pre-trained models and transfer learning to reduce the need for extensive manual annotation. The system leverages knowledge from pre-trained algorithms and applies it to specific biological image tasks, achieving high accuracy with minimal domain-specific training data and annotation effort.
Solution Approach 2:
The system uses copying by leveraging pre-trained models and transferring their learned representations to new tasks. Instead of training from scratch with extensive annotations, the patent copies knowledge from pre-trained algorithms and adapts it to specific biological image analysis tasks, significantly reducing annotation requirements while maintaining accuracy.
Data Source
AI summary
A system (100) comprises one or more processors (110) and one or more storage devices (120), wherein the system (100) is configured to generate a first high-dimensional representation of the biology-related language-based input training data (102) by a language recognition machine-learning algorithm executed by the one or more processors (110). Further, the system (100) is configured to generate biology-related language-based output training data based on the first high-dimensional representation by the language recognition machine-learning algorithm and adjust the language recognition machine-learning algorithm based on a comparison of the biology-related language-based input training data (102) and the biolo-gy-related language-based output training data. Additionally. the system (100) is configured to generate a second high-dimensional representation of the biology-related image-based input training data (104) by a visual recognition machine-learning algorithm executed by the one or more processors (110) and adjust the visual recognition machine-learning algorithm based on a comparison of the first high-dimensional representation and the second high-dimensional representation.