LC-MS Peak Sorting Using CNNs for Fast Protein Quantification

The patent replaces the manual mechanical process of expert peak selection with an automated deep learning system. The CNN-based model processes mass spectrometry data to automatically identify and select peaks, eliminating the need for manual inspection while maintaining expert-level accuracy. This substitution of human manual work with an automated computational system directly resolves the contradiction between high accuracy and long processing time.

The patent creates a computational model that learns from and copies the decision-making patterns of human experts. By training the deep learning model on data annotated by experts, the system replicates expert-level peak selection capabilities. This allows the system to achieve human expert accuracy automatically, without requiring actual human experts to perform the time-consuming manual selection process for each dataset.

The patent replaces simple automated algorithms with an advanced deep learning system that achieves both high speed and high accuracy. The CNN architecture, combined with data augmentation and transfer learning techniques, enables the system to process data rapidly while maintaining accuracy comparable to manual expert selection, thus resolving the trade-off between automation speed and selection precision.

The patent employs data augmentation techniques that transform and vary the training data parameters (such as adding noise, scaling, rotating spectra) to create more diverse training examples. This parameter transformation approach enables the model to learn more robust features and generalize better, improving accuracy while maintaining the automated high-speed processing capability.

The patent replaces semi-automated algorithms that require manual tuning and intervention with a fully automated deep learning system. The model automatically processes raw mass spectrometry data, performs peak detection, and generates results without requiring user interaction for parameter adjustment or manual peak selection, achieving complete automation while simplifying the operational workflow.

The patent implements a self-service system where the deep learning model autonomously performs all peak selection tasks without human intervention. The system self-adjusts parameters, self-optimizes performance through built-in validation, and self-completes the entire peak selection workflow, eliminating the need for manual operation and making the process truly automated and easy to use.