Cloud-Based Experimental Data Processing with Deep Learning Encoders
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Solution Overview
Problem
Current molecular data analysis systems face challenges in retaining and processing experimental data efficiently, leading to significant data loss and inaccurate false-discovery rate estimation, while failing to identify a substantial number of spectra and requiring custom preprocessing steps.
Innovation Solution
A cloud-based system utilizing deep learning encoders and shallow scoring models to process experimental data, enabling efficient molecule identification and quantification, and providing accurate false-discovery rate estimation without the need for decoys during training, while supporting multiple data types and acquisition modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current molecular data analysis systems process experimental data using traditional methods, then preprocessing steps can be completed, but significant data loss occurs and false-discovery rate estimation becomes inaccurate
Solution Approach 1:
The patent replaces traditional mechanical preprocessing steps with a cloud-based deep learning system. The encoder-based architecture automatically processes experimental data through neural network encoders that generate embeddings, eliminating the need for manual preprocessing steps that caused data loss and inaccurate FDR estimation. The system substitutes conventional signal processing mechanics with intelligent computational mechanics.
Solution Approach 2:
The system changes the fundamental parameters of data processing by transforming experimental data into embedding representations through trained encoders. This parameter transformation allows the system to retain more information while processing data more efficiently, improving both data retention and FDR estimation accuracy simultaneously.
2Productivity
If traditional preprocessing methods are used to prepare experimental data, then data can be processed, but a substantial number of spectra remain unidentified
Solution Approach 1:
The patent replaces traditional spectrum matching algorithms with a cloud-based deep learning system that uses encoders to generate embeddings for both experimental spectra and reference molecules. This substitution enables the system to identify spectra that traditional methods missed, improving the identification rate while reducing information loss.
Solution Approach 2:
The system segments the data processing task into distinct encoder components: an experimental data encoder for processing spectra, a molecule encoder for processing reference structures, and a scoring model for comparison. This segmentation allows each component to specialize in specific aspects of the identification process, improving overall productivity.
3Reliability
If custom preprocessing steps are implemented to improve data quality, then data accuracy may improve, but system complexity increases significantly
Solution Approach 1:
The patent merges multiple custom preprocessing steps into a single integrated cloud-based encoding system. The encoder architecture combines data normalization, feature extraction, and quality assessment into one unified process, reducing system complexity while maintaining or improving data quality through the collaborative work of multiple encoder components.
Solution Approach 2:
The cloud-based encoding system provides universal functionality that handles diverse experimental data types through the same encoder architecture. The system can process different data formats and experimental conditions without requiring separate custom preprocessing pipelines, reducing complexity through multi-functionality.
4Productivity
If deep learning encoders are used to process experimental data in the cloud, then computational efficiency increases and molecule identification accuracy improves, but data transmission and processing time may increase
Solution Approach 1:
The system performs preliminary encoding of reference molecule data into embeddings and stores them in the cloud database before actual experimental analysis. When experimental spectra are processed, the system retrieves pre-computed reference embeddings rather than computing them in real-time, significantly reducing processing time while maintaining high computational efficiency.
Solution Approach 2:
The patent introduces cloud-based encoding as an intermediary layer between data acquisition and analysis. The cloud encoding service acts as a mediator that receives raw experimental data, processes it through trained encoders, and returns results, allowing local systems to offload computationally intensive tasks and reduce their processing time.
Data Source
AI summary
The method for processing experimental data can include: determining experimental data (e.g., mass spectrometry spectra) and processing the experimental data. In variants, processing the experimental data can include: identifying one or more molecules, comparing experimental samples, determining a quantification, evaluating a quality of the experimental data, and/or otherwise processing the experimental data. The method can optionally include determining supplemental information, determining a set of candidate molecules, training a model, and/any other suitable steps.