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arXiv Machine Learning By Abdellah El Mekki, Laks V. S. Lakshmanan, Muhammad Abdul-Mageed

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for \textit{De Novo} Peptide Sequencing

Read the original on arXiv Machine Learning →

GyroNovo is a new framework for de novo peptide sequencing that improves fragment imputation by guiding the process with decoder errors observed during training. It introduces mass-aware attention using rotary embeddings to encode pairwise mass differences between spectral peaks, and creates easy and hard augmented views of spectra to train the decoder under varying corruption levels. Experiments on NovoBench demonstrate significant gains, with about 9 percentage points higher peptide-level precision and 7 percentage points higher amino-acid-level precision compared to the state-of-the-art baseline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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