Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries.Spectral libraries are useful resources in proteomic data analysis.  Recent advances in deep learning allow tandem mass spectra of peptides to be predicted from their amino acid sequences.  Here, we propose a deep learning-based full-spectrum prediction method to generate predicted spectral libraries for peptide identification. 
Deep Learning
Furthermore, merging spectra from different prediction models, as a form of ensemble learning, can produce improved spectral libraries, in terms of identification sensitivity. 
THERM