RT-GCTnovo

algorithm · Transformer (NAR)

RT-GCTnovo: algorithm · Transformer (NAR). Combines Transformer encoders with convolutional layers to capture global and local spectral features, fuses them through a sigmoid gate, adds retention time as a…

Combines Transformer encoders with convolutional layers to capture global and local spectral features, fuses them through a sigmoid gate, adds retention time as a constraint, and decodes semi-autoregressively with dynamic mass masks.

Kind algorithm
Deep learning yes
Acquisition DDA
Family Transformer (NAR)

Reported comparison

The comparison table this method’s own papers print, standardised: every value on a 0-1 scale, methods down the side, the measure and then the species across. These are numbers papers report about themselves and their baselines. They are not a leaderboard, and they do not compare across tables: each was produced by a different group, on the dataset named in its corner, with each baseline either retrained, run from released weights or quoted from another paper. Where the paper says which, it follows the method’s name (hover it for the sentence); most papers do not say. Bold is the best value in a column and underline the runner-up, our ranking rather than the paper’s own marks.

TABLE I

RT-GCTnovo: A Peptide De Novo Sequencing Model Incorporating Gated Multi-scale Features and Dynamic Mass Masks, page 6: Comparison of Average Accuracy (%)

Nine-species benchmark Peptide accuracy Amino acid accuracy
DeepNovo 0.376 0.673
Casanovo 0.449 0.678
TSARseqNovo 0.546 0.775
RT-GCTnovo 0.577 0.798

Paper describing it

Authors (5)

Nan Liu (Shandong Jianzhu University), Hang Zhang, Ningning Qiu, Chenghui Liu, Binhai Zhu (Montana State University)

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