Neural Network for MS1 Map Biomarker Classification
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Solution Overview
Problem
Current mass spectrometry-based proteomics methods are limited by their reliance on identified signals, ignoring un-ID-ed signals and using linear models that fail to exploit the full complexity of proteomic data, leading to challenges in biomarker discovery and data normalization across different experimental batches and instruments.
Innovation Solution
A computer-implemented method using a neural network to predict health conditions from mass spectrometry images, training on both identified and un-identified signals, and employing Deep Learning techniques to classify MS spectra, allowing for the recognition of discriminative features in proteomic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear models are used to analyze identified signals, then the analysis is simple and interpretable, but the sensitivity and specificity in biomarker discovery are limited
Solution Approach 1:
The patent replaces traditional linear statistical models with a neural network-based deep learning system. The neural network processes MS1 map images and raw proteomic data, automatically learning complex non-linear patterns that improve biomarker discovery sensitivity and specificity while handling the high-dimensional nature of proteomic data
2Loss of information
If only identified signals are used for analysis, then the data processing is straightforward, but many signals remain unused and information is lost
Solution Approach 1:
The patent extracts and utilizes previously unused un-identified signals from MS1 maps alongside identified peptide signals. The neural network processes the complete MS1 map image containing all signals, extracting discriminative features that would be missed by traditional approaches focusing only on identified peptides
Solution Approach 2:
The neural network serves multiple functions simultaneously: it processes both identified and un-identified signals, performs classification of health conditions, and identifies discriminative features. This multi-functional approach maximizes information utilization from the proteomic data
3Measurement precision
If deep learning techniques are employed to capture complex interrelations in proteomic data, then prediction accuracy improves, but the computational requirements and training time increase
Solution Approach 1:
The patent implements a two-stage training approach where the neural network is first pre-trained on a large dataset of MS1 maps to learn general proteomic patterns, then fine-tuned on smaller condition-specific datasets. This preliminary action reduces the computational burden and training time for achieving high prediction accuracy
Data Source
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AI summary
The present invention relates to a computer-implemented method for predicting a condition of health of a patient to be examined from a mass spectrometry, MS, specimen image representing a proteome of said patient. Also provided is a computer program product adapted to carry out the described method.