Biological Data Semantic Search via Machine Learning
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Solution Overview
Problem
The processing of vast amounts of biology-related data and the control of microscopes is time-consuming and inefficient, especially when dealing with unlabeled or untagged biological images, as existing methods rely heavily on manual analysis and require extensive annotation.
Innovation Solution
A system utilizing a trained language recognition machine-learning algorithm to generate high-dimensional representations of search data, allowing for the comparison and matching of textual and image-based data, enabling semantic searches across databases and microscope images without prior labeling, and providing control signals for microscope operations based on selected representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis methods are used to process biology-related data, then analysis accuracy can be maintained, but processing time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning system that uses trained algorithms to process and analyze biological data. The system substitutes human analysts with computational models that can rapidly process images and text data without manual intervention, thereby dramatically increasing processing speed while maintaining consistent analysis quality.
Solution Approach 2:
The machine learning system performs self-service by automatically analyzing biological data without requiring continuous human intervention. The trained models independently process images and text, generate results, and can even identify their own areas for improvement through continuous learning, reducing the need for ongoing manual analysis while maintaining high productivity.
2Measurement precision
If extensive annotation and labeling are performed on biological images, then search accuracy improves, but the complexity and time required for data preparation increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on large datasets before deployment. The models are prepared in advance with learned knowledge about biological structures and patterns, enabling them to accurately analyze and search through biological data without requiring extensive manual annotation of each individual image or text document during actual use.
Solution Approach 2:
The patent replaces the manual annotation process with automated machine learning-based tagging and classification systems. The trained models automatically generate metadata and annotations for biological images and text, substituting the complex manual labeling process with computational algorithms that can rapidly process data and generate accurate tags without human intervention.
3Adaptability or versatility
If traditional search methods are used for biological images, then exact matches can be found, but semantically similar images that were not labeled remain inaccessible
Solution Approach 1:
The system changes the search parameter from exact keyword matching to semantic similarity based on high-dimensional vector representations. By transforming images and text into comparable vector spaces using trained machine learning models, the system enables flexible searching that can retrieve semantically similar biological images even when exact keyword matches or labels are not present in the database.
Solution Approach 2:
The patent replaces traditional keyword-based search mechanisms with machine learning-based semantic search. The system uses trained models to understand the semantic content of biological images and text, enabling queries that can find conceptually similar data regardless of whether exact labels or keywords were assigned during data collection, thereby recovering otherwise inaccessible information.
Data Source
AI summary
Embodiments relate to a system (100) comprising one or more processors (110) and one or more storage devices (120). The system (100) is configured to receive biology-related language-based search data (101) and generate a first high-dimensional representation of the biology-related language-based search data (101) by a trained language recognition ma-chine-learning algorithm executed by the one or more processors (110). The first high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to obtain a plurality of second high-dimensional representations (105) of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets and compare the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations (105).