  As in many other scientific domains, we face a fundamental problem when using
machine learning to identify proteins from mass spectrometry data: large ground
truth datasets mapping inputs to correct outputs are extremely difficult to
obtain. Instead, we have access to imperfect hand-coded models crafted by
domain experts. In this paper, we apply deep neural networks to an important
step of the protein identification problem, the pairing of mass spectra with
short sequences of amino acids called peptides. We train our model to
differentiate between top scoring results from a state-of-the art classical
system and hard-negative second and third place results. Our resulting model is
much better at identifying peptides with spectra than the model used to
generate its training data. In particular, we achieve a 43% improvement over
standard matching methods and a 10% improvement over a combination of the
matching method and an industry standard cross-spectra reranking tool.
Importantly, in a more difficult experimental regime that reflects current
challenges facing biologists, our advantage over the previous state-of-the-art
grows to 15% even after reranking. We believe this approach will generalize to
other challenging scientific problems.
