Peptide-Spectra Matching with Weak Supervision
Abstract
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-theart
grows to 15% even after reranking. We believe this approach will generalize to
other challenging scientific problems.
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-theart
grows to 15% even after reranking. We believe this approach will generalize to
other challenging scientific problems.
Research Areas
