LIPNovo

algorithm · Latent imputation

LIPNovo: algorithm · Latent imputation. Latent imputation

Latent imputation

Kind algorithm
Deep learning yes
Acquisition DDA
Family Latent imputation

Code

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Reported comparisons (16)

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.

Table1 (HC-PT)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

ProteomeTools
HC-PT (NovoBench) 		Amino acid precision Amino acid recall Peptide precision Peptide AUC
DeepNovo · quoted 0.531 0.534 0.313 0.255
PointNovo · quoted 0.623 0.622 0.419 0.373
InstaNovo · quoted 0.289 0.285 0.057 0.034
Casanovo · quoted 0.442 0.453 0.211 0.177
AdaNovo · quoted 0.442 0.451 0.212 0.178
AdaNovo · retrained 0.492 0.496 0.289 0.254
π-HelixNovo · quoted 0.588 0.582 0.356 0.318
π-HelixNovo · retrained 0.532 0.537 0.301 0.261
Casanovo · retrained 0.525 0.530 0.324 0.290
LIPNovo 0.637 0.643 0.458 0.427

Table1 (Nine-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

Nine-species benchmark Amino acid precision Amino acid recall Peptide precision Peptide AUC
PEAKS · quoted 0.748 0.428
DeepNovo · quoted 0.696 0.638 0.428 0.376
PointNovo · quoted 0.740 0.671 0.480 0.436
InstaNovo · quoted 0.420 0.395 0.164 0.123
Casanovo · quoted 0.697 0.696 0.481 0.439
AdaNovo · quoted 0.698 0.709 0.505 0.469
AdaNovo · retrained 0.681 0.681 0.473 0.439
π-HelixNovo · quoted 0.765 0.758 0.517 0.453
π-HelixNovo · retrained 0.765 0.752 0.509 0.431
Casanovo · retrained 0.741 0.740 0.529 0.493
LIPNovo 0.797 0.797 0.582 0.547

In the paper: column amino acid precision: the original table only underlined π-HelixNovo (π-HelixNovo† (Yang et al., 2024)) (0.765); π-HelixNovo (π-HelixNovo (Yang et al., 2024)) (0.765) ties with it and is underlined here too.

Table1 (Seven-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

Seven-species benchmark Amino acid precision Amino acid recall Peptide precision Peptide AUC
DeepNovo · quoted 0.492 0.433 0.204 0.136
PointNovo · quoted 0.196 0.169 0.022 0.007
InstaNovo · quoted 0.192 0.176 0.031 0.009
Casanovo · quoted 0.322 0.327 0.119 0.084
AdaNovo · quoted 0.379 0.385 0.174 0.135
AdaNovo · retrained 0.403 0.405 0.189 0.149
π-HelixNovo · quoted 0.481 0.472 0.234 0.173
π-HelixNovo · retrained 0.465 0.462 0.218 0.156
Casanovo · retrained 0.357 0.366 0.159 0.119
LIPNovo 0.557 0.560 0.327 0.281

Table2 (HC-PT)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. † denotes our retrained results, and other results are given by NovoBench. The best results are marked in bold.

ProteomeTools
HC-PT (NovoBench) 		Ptm precision Ptm recall
DeepNovo · quoted 0.626 0.615
PointNovo · quoted 0.676 0.740
InstaNovo · quoted 0.350 0.261
Casanovo · quoted 0.501 0.460
AdaNovo · quoted 0.552 0.482
AdaNovo · retrained 0.562 0.532
π-HelixNovo · quoted 0.568 0.667
π-HelixNovo · retrained 0.632 0.566
Casanovo · retrained 0.550 0.582
LIPNovo 0.732 0.745

Table2 (Nine-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. † denotes our retrained results, and other results are given by NovoBench. The best results are marked in bold.

Nine-species benchmark Ptm precision Ptm recall
DeepNovo · quoted 0.576 0.529
PointNovo · quoted 0.629 0.546
InstaNovo · quoted 0.443 0.294
Casanovo · quoted 0.706 0.566
AdaNovo · quoted 0.652 0.570
AdaNovo · retrained 0.678 0.552
π-HelixNovo · quoted 0.680 0.598
π-HelixNovo · retrained 0.723 0.593
Casanovo · retrained 0.755 0.601
LIPNovo 0.765 0.656

Table2 (Seven-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. † denotes our retrained results, and other results are given by NovoBench. The best results are marked in bold.

Seven-species benchmark Ptm precision Ptm recall
DeepNovo · quoted 0.391 0.373
PointNovo · quoted 0.117 0.094
InstaNovo · quoted 0.126 0.115
Casanovo · quoted 0.360 0.251
AdaNovo · quoted 0.448 0.321
AdaNovo · retrained 0.430 0.356
π-HelixNovo · quoted 0.473 0.366
π-HelixNovo · retrained 0.362 0.370
Casanovo · retrained 0.368 0.292
LIPNovo 0.604 0.498

Table3

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 7: Leave-one-out cross validation compared to baseline on the Nine-species dataset. † means our re-trained results.

Nine-species benchmark Amino acid precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.743 0.754 0.761 0.769 0.754 0.753 0.753 0.717 0.751
LIPNovo 0.806 0.805 0.807 0.805 0.807 0.808 0.794 0.800 0.804
Nine-species benchmark Amino acid recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.745 0.752 0.757 0.770 0.754 0.752 0.755 0.716 0.750
LIPNovo 0.807 0.805 0.806 0.805 0.807 0.807 0.796 0.798 0.804
Nine-species benchmark Peptide precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.559 0.544 0.554 0.575 0.558 0.558 0.549 0.506 0.550
LIPNovo 0.607 0.591 0.606 0.596 0.596 0.607 0.577 0.577 0.595
Nine-species benchmark Peptide AUC
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.523 0.509 0.519 0.540 0.532 0.521 0.510 0.464 0.515
LIPNovo 0.581 0.563 0.577 0.567 0.565 0.579 0.545 0.544 0.565
Nine-species benchmark Ptm precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.797 0.742 0.762 0.784 0.779 0.799 0.760 0.710 0.767
LIPNovo 0.855 0.764 0.769 0.772 0.812 0.803 0.764 0.777 0.790
Nine-species benchmark Ptm recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.689 0.591 0.624 0.612 0.582 0.608 0.593 0.540 0.605
LIPNovo 0.739 0.647 0.675 0.672 0.630 0.667 0.634 0.627 0.661

Table4

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 7: Comparison with GraphNovo (Mao et al., 2023).

GraphNovo dataset and checkpoint Peptide recall Peptide AUC Amino acid recall Amino acid precision
GraphNovo · quoted 0.712 0.876 0.874
LIPNovo 0.729 0.707 0.876 0.882

In the paper: column peptide AUC: the original table bolded LIPNovo (0.707), the only value, since GraphNovo was not run; with nothing to rank it is not marked here.
In the paper: column amino acid recall: the original table only bolded LIPNovo (0.876); GraphNovo (0.876) ties with it and is bolded here too.

Table2 (HC-PT)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 15: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. The best and second-best results are marked in bold and underline, respectively.

ProteomeTools
HC-PT (NovoBench) 		Amino acid precision Amino acid recall Peptide precision Peptide AUC
DeepNovo 0.531 0.534 0.313 0.255
PointNovo 0.623 0.622 0.419 0.373
Casanovo · retrained 0.442 0.453 0.211 0.177
AdaNovo 0.442 0.451 0.212 0.178
π-HelixNovo 0.588 0.582 0.356 0.318
InstaNovo 0.289 0.285 0.057 0.034
SearchNovo 0.652 0.658 0.447 0.413
ReNovo 0.651 0.648 0.467 0.436
DiffuNovo 0.654 0.654 0.458 0.434
Casanovo retrained · retrained 0.525 0.530 0.324 0.290
LIPNovo 0.637 0.643 0.458 0.427
LIPNovo+ 0.698 0.694 0.519 0.490

In the paper: column amino acid precision: the original table underlined LIPNovo (0.637).

Table2 (Nine-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 15: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. The best and second-best results are marked in bold and underline, respectively.

Nine-species benchmark Amino acid precision Amino acid recall Peptide precision Peptide AUC
PEAKS 0.748 0.428
DeepNovo 0.696 0.638 0.428 0.376
PointNovo 0.740 0.671 0.480 0.436
Casanovo · retrained 0.697 0.696 0.481 0.439
AdaNovo 0.698 0.709 0.505 0.469
π-HelixNovo 0.765 0.758 0.517 0.453
InstaNovo 0.420 0.395 0.164 0.123
SearchNovo 0.748 0.746 0.550 0.489
ReNovo 0.770 0.769 0.568 0.528
DiffuNovo 0.791 0.789 0.565 0.536
Casanovo retrained · retrained 0.741 0.740 0.529 0.493
LIPNovo 0.797 0.797 0.582 0.547
LIPNovo+ 0.831 0.831 0.637 0.612

Table2 (Seven-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 15: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. The best and second-best results are marked in bold and underline, respectively.

Seven-species benchmark Amino acid precision Amino acid recall Peptide precision Peptide AUC
DeepNovo 0.492 0.433 0.204 0.136
PointNovo 0.196 0.169 0.022 0.007
Casanovo · retrained 0.322 0.327 0.119 0.084
AdaNovo 0.379 0.385 0.174 0.135
π-HelixNovo 0.481 0.472 0.234 0.173
InstaNovo 0.192 0.176 0.031 0.009
SearchNovo 0.489 0.488 0.259 0.174
ReNovo 0.512 0.514 0.278 0.228
DiffuNovo 0.437 0.435 0.193 0.162
Casanovo retrained · retrained 0.357 0.366 0.159 0.119
LIPNovo 0.557 0.560 0.327 0.281
LIPNovo+ 0.619 0.622 0.383 0.337

Table3 (HC-PT)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 16: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. The best and second-best results are marked in bold and underline, respectively.

ProteomeTools
HC-PT (NovoBench) 		Ptm precision Ptm recall
DeepNovo 0.626 0.615
PointNovo 0.676 0.740
InstaNovo 0.350 0.261
Casanovo · retrained 0.501 0.460
AdaNovo 0.552 0.482
π-HelixNovo 0.568 0.667
SearchNovo 0.715 0.772
Casanovo retrained · retrained 0.550 0.582
LIPNovo 0.732 0.745
LIPNovo+ 0.757 0.805

In the paper: column ptm recall: the original table underlined SearchNovo (0.772), LIPNovo (0.745).

Table3 (Nine-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 16: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. The best and second-best results are marked in bold and underline, respectively.

Nine-species benchmark Ptm precision Ptm recall
DeepNovo 0.576 0.529
PointNovo 0.629 0.546
InstaNovo 0.443 0.294
Casanovo · retrained 0.706 0.566
AdaNovo 0.652 0.570
π-HelixNovo 0.680 0.598
SearchNovo 0.764 0.599
Casanovo retrained · retrained 0.755 0.601
LIPNovo 0.765 0.656
LIPNovo+ 0.801 0.678

Table3 (Seven-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 16: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. The best and second-best results are marked in bold and underline, respectively.

Seven-species benchmark Ptm precision Ptm recall
DeepNovo 0.391 0.373
PointNovo 0.117 0.094
InstaNovo 0.126 0.115
Casanovo · retrained 0.360 0.251
AdaNovo 0.448 0.321
π-HelixNovo 0.473 0.366
SearchNovo 0.472 0.447
Casanovo retrained · retrained 0.368 0.292
LIPNovo 0.604 0.498
LIPNovo+ 0.648 0.561

Table4

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 17: Leave-one-out cross-validation on the Nine-species dataset.

Nine-species benchmark Amino acid precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.743 0.754 0.761 0.769 0.754 0.753 0.753 0.717 0.751
LIPNovo 0.806 0.805 0.807 0.805 0.807 0.808 0.794 0.800 0.804
LIPNovo+ 0.845 0.843 0.849 0.851 0.841 0.844 0.839 0.839 0.844
Nine-species benchmark Amino acid recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.745 0.752 0.757 0.770 0.754 0.752 0.755 0.716 0.750
LIPNovo 0.807 0.805 0.806 0.805 0.807 0.807 0.796 0.798 0.804
LIPNovo+ 0.844 0.842 0.848 0.851 0.840 0.843 0.839 0.838 0.843
Nine-species benchmark Peptide precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.559 0.544 0.554 0.575 0.558 0.558 0.549 0.506 0.550
LIPNovo 0.607 0.591 0.606 0.596 0.596 0.607 0.577 0.577 0.595
LIPNovo+ 0.669 0.657 0.679 0.673 0.657 0.658 0.653 0.652 0.662
Nine-species benchmark Peptide AUC
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.523 0.509 0.519 0.540 0.532 0.521 0.510 0.464 0.515
LIPNovo 0.581 0.563 0.577 0.567 0.565 0.579 0.545 0.544 0.565
LIPNovo+ 0.647 0.634 0.657 0.651 0.632 0.633 0.628 0.626 0.639
Nine-species benchmark Ptm precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.797 0.742 0.762 0.784 0.779 0.799 0.760 0.710 0.767
LIPNovo 0.855 0.764 0.769 0.772 0.812 0.803 0.764 0.777 0.790
LIPNovo+ 0.888 0.776 0.816 0.835 0.827 0.834 0.841 0.824 0.830
Nine-species benchmark Ptm recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.689 0.591 0.624 0.612 0.582 0.608 0.593 0.540 0.605
LIPNovo 0.739 0.647 0.675 0.672 0.630 0.667 0.634 0.627 0.661
LIPNovo+ 0.782 0.699 0.728 0.696 0.707 0.703 0.703 0.704 0.715

Table5

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 18: Comparison with state-of-the-art methods on GraphNovo dataset [11].

GraphNovo dataset and checkpoint Peptide recall Peptide AUC Amino acid recall Amino acid precision
PEAKS 0.475 0.758 0.775
Novor 0.431 0.740 0.753
DeepNovo 0.497 0.769 0.771
PointNovo 0.601 0.815 0.816
Casanovo · retrained 0.605 0.684 0.684
GraphNovo 0.712 0.876 0.874
LIPNovo 0.729 0.707 0.876 0.882
LIPNovo+ 0.740 0.720 0.889 0.891

Papers describing it (3)

Authors (7)

Ye Du, Chen Yang, Nanxi Yu, Wanyu Lin, Qian Zhao, Shujun Wang, Qian Niu

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