Exploring Trial-and-Error in Deep Learning: Initial Application to Isotope Detection in Mass Spectrometry

Mass spectrometry plays a crucial role in biomedicine by detecting isotopes, contributing significantly to research, diagnostics, and therapy development. This study introduces IsoFusion, a deep learning model designed to address isotope detection in raw mass spectra. Rather than directly applying convolutional layers to all signal and noise peaks, IsoFusion employs a trial-and-error strategy. First, it investigates all potential charge states (trials) and collects signal peaks around expected m/z values for each trial. Then, convolutional layers extract features from each trial, which are fused to identify the correct one. Finally, the reparameterization trick predicts isotope features based on this correct trial. A key advantage of IsoFusion is shared model parameters across all trials, enhancing feature learning for less common charge states using data from prevalent ones. Our results show a significant accuracy improvement for charge state 5, reaching 99.42%, compared to DeepIso's 43.36%. Moreover, IsoFusion achieves a 97.33% detection accuracy for isotopes, with 2.4% of detected isotopes previously unidentified by four commonly used methods.