A learned embedding for efficient joint analysis of millions of mass spectra

We propose to train a Siamese neural network using peptide–spectrum assignments to embed spectra in a low-dimensional space such that spectra generated by the same peptide are close to one another. We demonstrate that this learned embedding captures latent properties of the mass spectra, clusters related spectra in the low-dimensional space, and identifies the "dark matter" of the human proteome.