Perturbation Embeddings for Neuronal Cell Phenomic Images
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Solution Overview
Problem
Conventional systems for training machine learning models to classify neuronal cells suffer from inaccuracies, inefficiencies, and operational inflexibilities due to complex cell physiology, irregular plating patterns, and limited flexibility in analyzing atypical cell types, leading to inefficient drug discovery processes.
Innovation Solution
A perturbation embedding system utilizing a mixture of experts model combines phenomic embeddings from different models, such as masked auto-encoders and balanced supervised contrastive learning models, to generate a mixture of experts phenomap, determining combination weights based on benchmarking measures and phenoprint rates, and using an adapter model to differentiate perturbation impacts from confounding features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models are used to classify neuronal cells, then the classification process can be performed, but the accuracy is reduced due to complex cell physiology and irregular plating patterns
Solution Approach 1:
The patent combines multiple embedding models (masked auto-encoder and balanced supervised contrastive learning model) into a mixture of experts framework. Each model processes phenomic images independently to generate embeddings, which are then combined through weighted averaging to produce final classifications. This merging approach leverages the complementary strengths of different models to improve accuracy on complex neuronal cell data while managing individual model complexity.
Solution Approach 2:
The patent introduces phenomic embeddings as an intermediary representation layer between raw phenomic images and final cell classifications. The embedding models transform high-dimensional image data into compressed feature vectors that capture essential cellular characteristics. This intermediary step simplifies the classification task by working with condensed feature representations rather than raw pixels, improving both accuracy and computational efficiency.
2Adaptability or versatility
If conventional systems analyze atypical cell types like neuronal cells, then classification can be performed, but operational flexibility is reduced
Solution Approach 1:
The mixture of experts framework is designed to be universally applicable across different cell types including atypical neuronal cells. The system processes phenomic images from various cell types through the same embedding models and classification pipeline, adapting to different cell morphologies and plating patterns without requiring cell-type-specific model architectures. This universal approach enhances operational flexibility while maintaining productivity through automated processing.
Solution Approach 2:
The patent employs dynamic weighting in the mixture of experts model, where the contribution of each embedding model to the final classification can be adjusted based on performance metrics such as phenoprint rates and benchmarking measures. This dynamic adaptation allows the system to optimize its operation for different cell types and experimental conditions, improving both flexibility and drug discovery efficiency by automatically adjusting to the characteristics of the data being analyzed.
3Measurement precision
If conventional systems process phenomic images, then classifications can be generated, but computational resources are increased
Solution Approach 1:
The patent segments the overall classification task into independent embedding generation and classification stages. Each embedding model processes phenomic images separately to generate feature vectors, which are then combined and classified. This segmentation allows for efficient parallel processing and enables the use of pre-trained embedding models that can be reused across multiple classification tasks, reducing redundant computational work while maintaining high embedding accuracy.
Solution Approach 2:
The mixture of experts framework uses partial action by combining only the necessary components from multiple embedding models rather than using all possible features or models. The weighted combination approach selectively integrates embeddings based on their performance characteristics, avoiding the computational overhead of processing unnecessary features while maintaining accuracy. This partial action principle allows the system to achieve high embedding accuracy with optimized computational resource usage.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that train and utilize machine learning models to generate perturbation embeddings from phenomic images of cells, including neuronal cell images. Indeed, in one or more implementations, the disclosed systems generate a perturbation embedding using an adapter model or a mixture of experts model. In some implementations, the disclosed systems utilize a mixture of experts model that combines phenomic embeddings from different embedding models to generate a mixture of experts phenomap that contains information from multiple embedding models.