Perturbation Heatmap Embeddings for Cross-Experiment Genome Analysis
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Solution Overview
Problem
Conventional systems for analyzing biological relationships in digital data are inaccurate, inefficient, and inflexible, failing to accurately relate experimental data from disparate experiments and requiring excessive user interactions and queries to identify subtle relationships.
Innovation Solution
A perturbation mapping system that utilizes a machine learning model to embed phenomic digital images into a low-dimensional space, apply filtering, alignment, and aggregation models to generate a genome-wide perturbation database, and display interactive heatmaps for real-time analysis of perturbation relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems use brute-force approach to analyze digital biological data, then they can generate a large volume of user interfaces for data analysis, but they suffer from inaccuracy, inefficiency, and operational inflexibility
Solution Approach 1:
The patent replaces the conventional brute-force mechanical search approach with machine learning models that automatically learn and identify biological relationships from data. The system uses trained ML models to predict relationships between genes, diseases, and treatments without requiring manual querying or exhaustive searching, thereby improving accuracy while maintaining high productivity.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the raw digital biological data and the user interfaces. These models process and interpret the data, generating accurate relationship predictions that are then presented through user interfaces. This intermediary layer enables both high productivity and improved reliability by automating the analysis process.
2Measurement precision
If conventional systems perform exhaustive search analysis, then they can comprehensively analyze digital results, but they require excessive user interactions and queries
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on comprehensive digital biological data before actual use. The models learn relationships and patterns in advance, enabling them to provide comprehensive analysis results instantly without requiring users to perform exhaustive searches or multiple queries, thus eliminating time loss while maintaining comprehensiveness.
Solution Approach 2:
The system enables self-service by allowing the machine learning models to automatically perform comprehensive data analysis without requiring user interactions or queries. The models independently process digital biological data, generate relationship predictions, and present results through user interfaces, freeing users from time-consuming manual analysis tasks.
3Device complexity
If conventional systems use fixed analysis methods, then they are simple to implement, but they lack operational flexibility to adapt to different biological data types
Solution Approach 1:
The patent implements universality by designing machine learning models that can handle multiple types of digital biological data (genes, diseases, treatments, phenotypic images) with a single unified framework. The models are trained on diverse data types and can adapt to analyze different biological relationships without requiring separate systems or complex customization, achieving both simplicity and versatility.
Solution Approach 2:
The patent applies parameter changes by adjusting model parameters and configurations based on the specific type of biological data being analyzed. The system maintains a core unified ML framework but dynamically modifies parameters to optimize performance for different data types and relationship queries, enabling operational flexibility while keeping the overall system simple and consistent.
4Ease of manufacture
If conventional systems present data in static formats, then they are easy to generate, but they cannot support real-time identification of subtle biological relationships
Solution Approach 1:
The patent introduces dynamics by implementing interactive and dynamic user interfaces that respond to user inputs and queries in real-time. The machine learning models continuously process new data and update relationship predictions dynamically, allowing the system to identify subtle biological relationships as they emerge from the data, while maintaining ease of interface generation through automated design processes.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for embedding perturbation data via a machine learning model and filtering, aligning, and aggregating the embeddings to generate a genome-wide perturbation database for real-time generation of perturbation heatmaps. In particular, in one or more embodiments, the disclosed systems can receive a plurality of perturbation images portraying cells from a plurality of wells corresponding to a plurality of cell perturbations. Further, the systems can generate, utilizing a machine learning model, a plurality of well-level image embeddings from the plurality of perturbation images. Moreover, the systems can align, utilizing an alignment model, the plurality of well-level image embeddings to generate aligned well-level image embeddings. Additionally, the systems can aggregate, according to perturbations of one or more perturbation experiments, the well-level image embeddings to generate perturbation-level image embeddings. Furthermore, the systems can generate perturbation comparisons utilizing the perturbation-level image embeddings.