BiATNovo

algorithm · Transformer (AR)

BiATNovo: algorithm · Transformer (AR). Bidirectional self-attention

Bidirectional self-attention

Kind algorithm
Deep learning yes
Acquisition both
Family Transformer (AR)

Code

Live stars, open issues and last-push figures are on the Code activity chart.

Data

What the papers describing this method trained it on and reported its results on, as each paper states it. A version is named only where the paper names one.

Trained on

Evaluated on

Benchmarks

  • denovo_benchmarks: median peptide-level average precision 0.469 over 86 datasets, median rank 12 of 15 (version bm-1.0.0).

Both are mass-based matches on the tool’s most recent run. What these numbers mean.

Reported comparisons (2)

The comparison tables 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 2

BiATNovo: A Self-Attention based Bidirectional Peptide Sequencing Method, page 7: Results of BiATNovo and Baseline.

De novo sequencing of DIA data Amino acid precision Amino acid recall Peptide precision
OC UTI Plasma OC UTI Plasma OC UTI Plasma
DeepNovo-DIA 0.6812 0.6581 0.6379 0.6822 0.6598 0.6430 0.5240 0.5106 0.3740
BiATNovo 0.7457 0.7078 0.6628 0.7513 0.7151 0.6707 0.6147 0.5760 0.4364

Table 2

BiATNovo: An Attention-based Bidirectional De Novo Sequencing Framework for Data-Independent-Acquisition Mass Spectrometry, page 8: Comparison of DeepNovo-DIA, PepNet and BiATNovo

De novo sequencing of DIA data Amino acid precision Amino acid recall Peptide precision
OC UTI Plasma OC UTI Plasma OC UTI Plasma
DeepNovo-DIA 0.5562 0.6286 0.4354 0.5596 0.6243 0.4335 0.5098 0.5525 0.3322
BiATNovo 0.6817 0.7108 0.4525 0.6746 0.7076 0.4511 0.6439 0.6584 0.3691
PepNet 0.4008 0.4004 0.3064

Papers describing it (2)

Authors (11)

Siyu Wu, Zhongzhi Luan, Zhenxin Fu, Qunying Wang, Tiannan Guo, Shu Yang, Binyang Li, Yuxiaomei Liu, Fangzheng Li, Jiaxing Qi, Xiaohui Liang

Seen in the charts

Back to the full map

Back to top