Fig. 1
From: A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data
Overall pipeline of MS1Former. (A1) Tissue samples are hydrolyzed into peptide using trypsin, MS data was acquired using four different mass spectrometry instruments containing nano-electrospray ionization (Nano-ESI), (A2) The MS data was visualized, and (A3) Pattern detection for tumor and non-tumor(normal) samples across m/z dimension, the heatmaps show the distribution of the intensities of spectra data, where the color from deep viridis to deep yellow means that the intensity values change from low to large. The density graphs show the distribution of each channel’s intensities, for example, the right density plots show the intensity distribution of 340-360 across the m/z dimension. (B) Data processing, the front tail and rear tail (highlighted in two red boxes) was removed using the adapted noise removal method; subsequently, binning and normalization on m/z were performed to obtain the processed MS data. (C) MS1Former model was trained to diagnose HCC based on input binned m/z sequences. By using the combination of 1d-convolutional neural network (1d-CNN) and transformer encoding module, it achieve an accurate prediction for HCC, which was further verified through the distinguishable features (m/z, intensity), as seen in the red box. The MS data visualization results are generated using ProteoWizard 3.038, and the overall graph is created using Figma online.
