Phenomic-Transcriptomic Maps Using Embeddings for Perturbation Analysis
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Solution Overview
Problem
Conventional systems face inaccuracies, inflexibility, and inefficiencies in analyzing biological data from perturbation experiments, failing to accurately relate interrelationships and requiring numerous user interactions to benchmark biological signals.
Innovation Solution
A machine learning mapping system that embeds perturbation experiment unit measurements into a low-dimensional space, applies filtering, aligning, and aggregation models, and generates combined phenomic-transcriptomic maps to display perturbation comparisons and benchmark measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems analyze biological data from perturbation experiments, then biological treatment predictions can be generated, but accuracy is insufficient and interrelationships cannot be accurately related
Solution Approach 1:
The patent combines phenomic data and transcriptomic data into a unified digital map, integrating multiple data types to improve both measurement precision and reliability. The system merges embeddings from different biological assays to create a comprehensive view of biological interrelationships, resolving the contradiction by using combined data sources rather than isolated analyses
Solution Approach 2:
The patent introduces digital embeddings as an intermediary representation layer between raw biological data and analysis results. These embeddings serve as a mediator that captures essential features while reducing noise, thereby improving both the precision of measurements and the reliability of identified interrelationships without requiring direct comparison of raw data
2Adaptability or versatility
If conventional systems implement computing devices for biological data analysis, then predictions can be generated, but flexibility is limited
Solution Approach 1:
The patent creates a universal digital map framework that can accommodate multiple types of biological assays and data formats. The system is designed to handle phenomic, transcriptomic, and other biological data types through a common embedding and comparison architecture, providing flexibility without proportionally increasing complexity through standardized interfaces and unified processing pipelines
3Productivity
If conventional systems analyze biological data, then treatment predictions can be generated, but efficiency is reduced and computational time increases
Solution Approach 1:
The patent performs preliminary embedding of biological data into compressed digital representations before conducting analyses. By pre-processing data into embeddings that capture essential features in reduced dimensionality, the system enables faster comparisons and predictions, significantly reducing computational time while maintaining analytical efficiency
Solution Approach 2:
The patent creates digital copies of biological data in the form of embeddings that replicate essential biological information in a computationally efficient format. These digital embeddings serve as lightweight copies that can be rapidly processed and compared, improving efficiency without requiring analysis of the full complexity of original biological datasets
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. For example, the disclosed systems can generate a combined phenomic-transcriptomic map from embedding perturbation data via a machine learning model and filtering, aligning, aggregating, and relating the embeddings to generate transcriptomic comparisons. Additionally, the disclosed systems can embed phenomic perturbation data via a machine learning model and filtering, aligning, aggregating, and relating the phenomic perturbation embeddings to generate phenomic perturbation comparisons. Furthermore, the disclosed systems can utilize transcriptomic comparisons determined from aggregated transcriptomic embeddings and phenomic embedding comparisons determined from aggregated phenomic perturbation embeddings to generate combined phenomic-transcriptomic maps of biology. In some implementations, the disclosed systems generate a combined phenomic-transcriptomic map that reflects joint similarities between perturbation classes across transcriptomic comparisons and the phenomic embedding comparisons.