Neural Network for Proteomic Data Homogenization
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Solution Overview
Problem
Current methods for processing large-scale proteomic data face challenges in integrating data across different platforms, users, laboratories, and locations due to technical confounding factors such as variations in instrumentation, location, time, and user involvement.
Innovation Solution
The use of a neural network to remove technical variation from proteomic datasets by training to decrease a loss function that increases similarity between latent embeddings from the same sample and decreases similarity between latent embeddings from different samples, thereby identifying polyamino acid descriptors associated with biological states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If proteomic data is integrated across different platforms, users, laboratories, and locations, then the quantity and diversity of available data increases, but technical confounding factors such as variations in instrumentation, location, time, and user involvement introduce noise and reduce data quality
Solution Approach 1:
The patent extracts and removes technical confounding factors (instrumentation, location, time, user variations) from the proteomic data through computational methods, separating these non-biological variations from the biological signals of interest. This allows the data to be cleaned of technical noise while preserving the underlying biological information across multiple sources.
Solution Approach 2:
The patent introduces computational intermediaries (algorithms, statistical models, machine learning tools) that act as mediators between the raw proteomic data from different sources and the final biological insights. These intermediaries process and harmonize the data, transforming it from a noisy multi-source format into a cleaned, comparable format that reveals biological patterns.
2Stability of the object's composition
If technical variation is removed from proteomic datasets, then data homogenization improves and biological signals become clearer, but the complexity of data processing and computational requirements increase
Solution Approach 1:
The patent segments the complex data processing task into distinct modular steps: data collection from multiple sources, quality control, normalization, batch effect correction, and biological analysis. Each step handles a specific aspect of the data, making the overall complex process more manageable and reproducible through systematic decomposition.
Solution Approach 2:
The patent employs parameter changes and transformations (normalization, scaling, log-transformation, batch effect correction algorithms) to modify the characteristics of the data parameters. These transformations adjust the scale, distribution, and relationships between variables to reduce technical variation while preserving biological signals, thereby homogenizing the data without requiring complex processing architectures.
3Measurement precision
If latent embeddings are optimized to increase similarity for same-sample data and decrease similarity for different-sample data, then separation of biological signals from technical noise improves, but the computational time and training requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing the data (normalization, feature selection, quality control) before training the latent embedding models. This preliminary preparation reduces the complexity of the training task and allows the models to focus computational resources on learning the essential biological patterns rather than processing raw, unprocessed data, thereby reducing overall training time.
Solution Approach 2:
The patent applies partial action by training latent embedding models that focus on capturing only the most significant biological variations rather than all possible data characteristics. The models are designed to prioritize biologically relevant patterns while ignoring redundant or noisy information, achieving effective signal separation with reduced computational effort compared to analyzing all data dimensions equally.
Data Source
AI summary
In some aspects, the present disclosure provides a method for determining a polyamino acid descriptor associated with a biological state. The method can comprise removing technical variation from a proteomic dataset to generate a refined proteomic dataset, the technical variation arising from a predetermined non-biological factor, by training a neural network using a loss function. The loss function can be configured to increase a similarity between a first set of latent embeddings that are based on a first subset of polyamino acid descriptors in the proteomic dataset, wherein the first subset of polyamino acid descriptors are obtained from the same sample. The loss function can be configured to decrease the similarity between a second set of latent embeddings that are based on a second subset of polyamino acid descriptors in the proteomic dataset, wherein the second subset of polyamino acid descriptors are obtained from different samples.