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Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read De novo peptide sequencing improves when a reranker selects among candidates from several sequencing models: RankNovo reports 0.660 average peptide recall on the nine-species benchmark, 6.1% above its best base model.

desk verdict A genuinely new reranking idea for de novo peptide sequencing with sensible mass-based losses, but the headline SOTA claim needs an oracle-recall baseline before it fully holds up. read the letter →

arxiv 2505.17552 v2 pith:NZDW42EM submitted 2025-05-23 cs.LG cs.AI

Zijie Qiu , Jiaqi Wei , Xiang Zhang , Sheng Xu , Kai Zou , Zhi Jin , Zhiqiang Gao , Nanqing Dong
show 1 more author
Siqi Sun
This is my paper · ORCID
classification cs.LGcs.AI
keywords denovopeptidesequencingrerankingmassspectrometrymultiplesequencealignmentaxialattentiondeviationproteomicszero-shotgeneralization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

RankNovo treats de novo peptide sequencing—reading a peptide's amino-acid sequence directly from a tandem mass spectrum, without a reference database—as a reranking problem. Instead of trusting any single sequencing model, the paper trains a list-wise reranker that takes the candidate peptides produced by several base models, lets the candidates compare with each other in a single pass, and picks the best one. On the standard nine-species benchmark the reranker reports an average peptide recall of 0.660, beating its strongest base model by 6.1% and the previous state of the art by 4.3%, with similar gains on the newer V2 benchmark. The paper's broader claim is that reranking is a universal layer: trained on just two weak models, RankNovo still improves candidates from unseen models, so it could sit on top of any future sequencer. If this holds, the single-model paradigm of the field is challenged; accuracy becomes something you buy by adding models to the pool and a skilled selector on top.

What carries the argument

The load-bearing object is the candidate panel: for each spectrum, the outputs of several base sequencers are padded to equal length, stacked into a $c \times \ell \times d$ tensor—a multiple sequence alignment of peptides—and processed in one forward pass. Axial attention alternates row attention (within one candidate) and column attention (across candidates), so the score of each candidate depends on its rivals, at cost $O(c\ell^2 + k^2\ell)$ instead of full self-attention over all tokens; cross-attention injects the spectrum encoder's features. The training signal is the pair of mass-deviation metrics: PMD runs a Needleman–Wunsch-style dynamic program with residue mismatch cost $|M(r_i)-M(r_j)|$ and a gap penalty set to the average mass divergence between distinct residues, while RMD compares each query residue's prefix mass to the nearest target prefix mass. The joint objective is $L = \lambda L_{\mathrm{PMD}} + (1-\lambda)L_{\mathrm{RMD}}$ with $\lambda=0.5$, both terms RMSE. These metrics replace binary correctness labels, giving the reranker a graded, mass-aware notion of how wrong each candidate is.

What would settle it

Compute, on the 9-species-V1 test set, the oracle peptide recall of the pooled beam candidates from the six base models—the fraction of spectra for which the correct peptide appears anywhere in the pool. If that oracle recall already reaches or exceeds RankNovo's 0.660, or if a trivial selector that always takes the longest or most frequent candidate matches its performance, the claim that learned list-wise reranking drives the improvement is refuted; if oracle recall is high and RankNovo falls far below it, the reranker's selection is the true bottleneck and the architecture claim stands.

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Extended reading notes

Core claim

The paper's central claim is that the accuracy ceiling of de novo peptide sequencing is not set by any single model: different sequencers make different correct predictions on different spectra, and a meta-model that compares all candidates at once can recover more correct peptides than any of its generators. RankNovo, presented as the first deep reranking framework for this task, stacks the candidate peptides from six base models into a multiple sequence alignment and processes the stack with axial attention—row attention reads each candidate, column attention lets candidates share information—with cross-attention to the spectrum. Supervision comes from two new mass-based scores: PMD, a peptide-level divergence computed by dynamic-programming alignment in which mismatches cost the mass difference between residues, and RMD, a residue-level vector of prefix-mass deviations. Trained to predict both with a joint RMSE loss, RankNovo reports average peptide recall of 0.660 on 9-species-V1 and 0.781 on 9-species-V2, surpassing every base model it was trained on. Ablations attribute part of the gain to the mass-based objectives: identical backbones trained with point-wise, pair-wise, or standard list-wise losses reach only about 0.646–0.648 peptide recall, while PMD+RMD reaches 0.660, and removing column attention drops recall to 0.653. When trained on the two weakest models, the reranker still improves candidates from four unseen models, with recall rising from 0.586 to 0.649 as unseen models are added.

Load-bearing premise

The load-bearing premise is that the candidate pool usually contains the correct peptide: the paper never reports oracle recall or the number of candidates per spectrum, so part of the measured gain could come from pooling more hypotheses rather than from any learned ranking skill.

Editorial extensions

If this is right

  • Because the reranker generalizes zero-shot, a new sequencing model can be added to the pool at inference time with no retraining; the paper shows recall rising from 0.586 to 0.649 as unseen models are added to a two-model training set.
  • Accuracy becomes tunable against compute: more base models cost more candidate-collection time (roughly linear, from 0.258 to 0.949 seconds per spectrum for 2 to 6 models) but yield higher recall, a trade-off no single-model sequencer offers.
  • The mass-deviation supervision transfers to post-translational-modification settings: the paper reports gains of 5.6% on acetylation, 2.8% on dimethylation, and 6.7% on phosphorylation over the best base model after fine-tuning.
  • Reranking improvements and single-model improvements compound: any future stronger base model becomes a stronger candidate generator, so the reranking layer's value increases with the diversity and quality of the pool.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper never reports oracle recall—how often the correct peptide appears in the candidate pool at all. A decisive follow-up would compute the best-in-pool recall of the six base models' beams on 9-species-V1; if that number is close to 0.660, the reranker's value is modest selection over a rich pool, while a large gap between oracle and achieved recall would show selection skill is the real cei
  • The MSA-style formulation suggests a natural extension beyond de novo models: the same panel could rerank peptide-spectrum matches produced by database search engines, merging two traditionally separate identification paradigms.
  • A cheap test of the 'universal reranker' claim would be to train on the two weakest models and then add a qualitatively different sequencer family that was never seen, such as a non-autoregressive or graph-based model; the paper's zero-shot trend predicts continued recall gains.
  • If the list-wise design is what matters, the number of candidates per spectrum should be reported and varied; the paper fixes beam size at 5 without stating how many candidates actually enter the panel, and that number is the knob controlling both performance and the inference-time trade-off the paper emphasizes.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes RankNovo, a list-wise deep reranking framework for de novo peptide sequencing. Six base sequencing models (Casanovo-V2, ContraNovo, ByNovo, R-Casanovo, R-ContraNovo, R-ByNovo) generate candidate peptides; RankNovo represents the candidates as a multiple sequence alignment, applies axial attention (row and column) plus cross-attention to spectrum features, and is trained with two new mass-based losses, PMD (peptide mass deviation) and RMD (residual mass deviation). Evaluation on the 9-species-V1 and 9-species-V2 benchmarks reports average peptide recall of 0.660 and 0.781, respectively, exceeding all base models and prior published methods, together with a zero-shot experiment in which a RankNovo trained on two base models is applied to candidates from additional unseen models.

Significance. If the central claim is established, RankNovo would be a meaningful contribution: it introduces a reranking paradigm to a field dominated by single-model architectures, proposes interpretable mass-deviation supervision that improves over generic pointwise/pairwise/listwise losses, demonstrates transfer to unseen base models, and ships code. The paper also provides extensive ablations (loss terms, column attention, base-model subsets) and reports inference cost. However, the main quantitative claim is currently confounded by candidate-pool expansion: RankNovo reranks beam-5 outputs from up to six models, while base-model numbers are top-1 predictions, and no oracle-recall or best-in-beam control is reported. The significance will be much clearer once the authors quantify how much of the gain comes from having more candidates versus from selecting correctly among them.

major comments (4)
  1. [Section 4.2, Appendix A] The evaluation does not separate reranking skill from candidate-pool expansion. RankNovo reranks the beam-5 outputs of six base models (up to 30 candidates per spectrum), whereas the base-model peptide recalls in Table 1 are top-1 predictions. The paper never reports the number of candidates per spectrum that actually enter the reranker, the oracle recall of that pooled set, or the best-in-beam recall of each individual base model. Without these numbers, the observed gain (0.660 vs 0.623 for ByNovo) could be explained by a selector that benefits only from a larger pool, not from ranking quality. Please report oracle recall and best-in-beam recall for each base model and for the full pool, and add a pool-only baseline such as 'always choose the strongest base model's top-1' or 'choose the longest candidate' to quantify the skill component.
  2. [Section 4.3, Appendix E.1, Table 16] The zero-shot experiment is subject to the same confound. Table 16 shows peptide recall rising from 0.586 with 2 inference models to 0.649 with 6 inference models, but the candidate pool also grows from 10 to 30 hypotheses per spectrum. The paper attributes this improvement to RankNovo's zero-shot reranking ability, yet without oracle recall at each inference-model count, or a nonparametric baseline that always selects ByNovo's top-1 (which is already 0.623), the gain could be due mainly to the addition of stronger candidates. Please report oracle recall for each N-infer setting and a 'best base model top-1' baseline.
  3. [Section 4.2, Table 1, Table 6] There is a numerical inconsistency in the reported amino acid precision: the main text states 'RankNovo reaches a precision of 0.829,' while Table 1 and Appendix Table 6 both report an average of 0.824 on 9-species-V1. This discrepancy affects the paper's headline comparison with ByNovo (0.804) and ContraNovo (0.784). Please correct the text and re-verify the summary statistics, including the percentage gains quoted for peptide recall.
  4. [Section 4.4, Tables 9 and 10] The base-model-combination ablations change both the training set and the inference candidate-pool size simultaneously, so they do not isolate the effect of model diversity. A reader cannot tell whether the monotonic improvement from 0.586 (2 models) to 0.660 (6 models) is due to more diverse training candidates, more candidates at inference, or better reranking. Please add a control with a fixed inference pool (e.g., always 6 models) while varying the training set, and a control with a fixed training set while varying the inference pool, and report oracle recall for each condition.
minor comments (4)
  1. [Introduction, Related Work] The abstract and introduction call RankNovo 'the first deep reranking framework' for de novo peptide sequencing, but the references include pNovo-3 (Yang et al., 2019), which is described as a learning-to-rank framework for exactly this task. Please qualify the novelty claim by contrasting with pNovo-3 explicitly.
  2. [Appendix A] The training-set construction excludes 'spectrums that are correctly predicted by all six base models,' leaving 7 million spectra. The potential selection bias introduced by this filter is not discussed; please comment on whether this affects the learned reranking behavior and whether the evaluation set is also filtered.
  3. [Section 3.2, Equation (1)] The sinusoidal m/z embedding in Equation (1) uses the symbol k both as an index in the sine/cosine argument and as the dimension divisor in the denominator; this is confusing and should be clarified.
  4. [Appendix E.4, Figure 7] The 'contribution of each base model' analysis is based only on spectra where RankNovo is correct and exactly one base model contains the correct peptide in its beam. This is a narrow subset and does not measure each model's marginal contribution to overall recall; a leave-one-out ablation would be more informative.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PMD/RMD labels are defined from the amino-acid mass table and ground-truth peptides, and RankNovo is evaluated on external held-out benchmarks.

full rationale

RankNovo's derivation chain is self-contained. The supervision labels PMD and RMD are defined directly from the amino-acid mass table M:R->R+ and the ground-truth peptide via dynamic programming alignment and prefix-mass matching (Eqs. 4-7); they contain no fitted parameters and do not presuppose RankNovo's outputs. The model is trained on MassIVE-KB to regress these labels and is evaluated on held-out 9-species-V1/V2 benchmarks, so the reported peptide-recall gains are not statistically forced by construction. Self-trained base models (ByNovo, R-Casanovo, R-ContraNovo, R-ByNovo) supply the candidate pool, but their outputs are inputs to the reranker, not conclusions derived from it; reliance on in-house components is self-reliance, not circular reasoning. The zero-shot experiment (Appendix E.1, Table 16) trains on two base models and reranks candidates from up to four additional models not seen during training, which is a genuine out-of-distribution test rather than a tautology. The main evidentiary weakness, that oracle recall and per-candidate counts are not reported so pool expansion is not separated from ranking skill, concerns attribution of the empirical improvement, not a reduction of the derivation to its inputs. The reported amino-acid precision discrepancy between Section 4.2 (0.829) and Table 1 (0.824) is a numerical inconsistency, not a circular step. No equation in the paper is equivalent to its own input by construction, and no load-bearing claim rests on a self-citation chain.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

No fitted physical constants or invented entities are introduced. The framework consumes a candidate pool produced by six base models, four of which the authors trained, plus the ground-truth labels from public datasets. PMD and RMD are derived from the amino acid mass table and ground truth rather than fitted to the target result.

free parameters (3)
  • Loss weight λ in joint loss L = λ L_PMD + (1-λ) L_RMD = 0.5
    Equation 10 sets λ=0.5 by hand; no sensitivity ablation is reported, so the claimed benefit of combining PMD and RMD is only tested at this one setting.
  • Beam size and candidate set size for evaluation = beam size 5; exact candidate count unspecified
    Section 4.1 specifies beam search with size 5 but not how many candidates per spectrum are reranked; this choice directly controls the size of the candidate pool and is never ablated.
  • Training candidate filtering = spectra correct for all six base models excluded
    Appendix A removes about 23 million spectra from MassIVE-KB before training; the effect of this filter on RankNovo's performance is not analyzed.
assumptions (6)
  • domain assumption The amino acid mass table M(r) is accurate for all residues and PTMs.
    PMD and RMD labels are computed from this table (Eq. 4 and Section 3.3); if masses are wrong, the supervision signal is noisy.
  • domain assumption Ground-truth peptide labels in MassIVE-KB and the 9-species datasets are correct at the stated FDR.
    All training targets and evaluation metrics depend on these labels being essentially correct.
  • standard math Needleman-Wunsch dynamic programming with mass substitution costs defines a meaningful peptide distance.
    PMD inherits this assumption from the recurrence in Eq. 6.
  • domain assumption The candidate pool from the base models contains the correct peptide for a large fraction of test spectra.
    If the correct peptide is absent from the reranking candidate set, RankNovo cannot recover it; this is the load-bearing premise of any reranking system.
  • domain assumption The six base models are trained on MassIVE-KB without test leakage into the 9-species evaluation sets.
    The base model selection criteria in Appendix B.3 assert this, but the claim is not independently verified in the paper.
  • domain assumption Column-wise axial attention and cross-attention are sufficient to extract cross-candidate and spectrum features.
    The architecture assumes that the MSA-style representation makes candidate comparisons learnable from the listed components.

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Cite this review

Pith. "Pith review of Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing." pith.science (2026). https://pith.science/paper/NZDW42EM

@misc{pith2026250517552,
  author       = {Pith},
  title        = {Pith review of: Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NZDW42EM}},
  note         = {Machine review of arXiv:2505.17552}
}
read the original abstract

De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrometry data and the heterogeneous distribution of noise signals, leading to data-specific biases. We present RankNovo, the first deep reranking framework that enhances de novo peptide sequencing by leveraging the complementary strengths of multiple sequencing models. RankNovo employs a list-wise reranking approach, modeling candidate peptides as multiple sequence alignments and utilizing axial attention to extract informative features across candidates. Additionally, we introduce two new metrics, PMD (Peptide Mass Deviation) and RMD (residual Mass Deviation), which offer delicate supervision by quantifying mass differences between peptides at both the sequence and residue levels. Extensive experiments demonstrate that RankNovo not only surpasses its base models used to generate training candidates for reranking pre-training, but also sets a new state-of-the-art benchmark. Moreover, RankNovo exhibits strong zero-shot generalization to unseen models whose generations were not exposed during training, highlighting its robustness and potential as a universal reranking framework for peptide sequencing. Our work presents a novel reranking strategy that fundamentally challenges existing single-model paradigms and advances the frontier of accurate de novo sequencing. Our source code is provided on GitHub.

Figures

Figures reproduced from arXiv: 2505.17552 by the authors.

Figure 1
Figure 1. (A) De Novo Peptide Sequencing Workflow Using Tan￾dem Mass Spectrometry: Our objective is to predict peptide se￾quences from MS/MS spectra, as illustrated in the final two steps. (B) Motivation for RankNovo: Current de novo peptide sequenc￾ing models exhibit data preference in their peptide predictions. RankNovo improves overall prediction accuracy by reranking the outputs of these models to identify the optimal seq… view at source ↗
Figure 2
Figure 2. An overview of the RankNovo architecture. (A) Multiple base models generate peptide sequence candidates from the spectrum input, which are subsequently reranked by RankNovo. (B) The architecture of RankNovo incorporates a multi-peptide encoder, utilizing axial attention along both row and column dimensions and cross-attention to effectively integrate spectrum features. (C) The coarse-grained PMD metric assesses pept… view at source ↗
Figure 3
Figure 3. (A) Zero-shot performance of RankNovo when trained on two base models. (B) Unique-correctly selected percentage of base models. (C) Influence of peptide length. (D) The performance comparison of amino acids with similar masses. models into the candidate sets during inference ( [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The architecture of six base models for de novo peptide sequencing. 1. CasaNovo V2: This fundamental Transformer-based de novo sequencing model treats de novo sequencing as a sequence-to-sequence machine translation task, translating spectra into peptides. 2. ContraNov…
Figure 5
Figure 5. Figure 5: The performance comparison of amino acids with similar masses. The numbers are calculated on the 9-species-V1 dataset. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]
Figure 6
Figure 6. Figure 6: Influence of peptide length on 9-species-V1 dataset. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_6.png]
Figure 7
Figure 7. Figure 7: Unique-correctly selected percentage of base models. The numbers are calculated on the 9-species-V1 dataset. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]

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Works this paper leans on

68 extracted references · 49 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    J., Bambrick, J., et al

    Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. Accurate structure prediction of biomolecular interactions with alphafold 3. Nature, pp.\ 1--3, 2024

    2024

  3. [3]

    and Mann, M

    Aebersold, R. and Mann, M. Mass spectrometry-based proteomics . Nature, 422 0 (6928): 0 198--207, 2003. ISSN 1476-4687. URL https://doi.org/10.1038/nature01511

    doi:10.1038/nature01511 2003

  4. [4]

    H., Bilmes, J., and Noble, W

    Bittremieux, W., May, D. H., Bilmes, J., and Noble, W. S. A learned embedding for efficient joint analysis of millions of mass spectra. Nature methods, 19 0 (6): 0 675--678, 2022

    work page 2022

  5. [5]

    A., Tabb, D

    Breci, L. A., Tabb, D. L., Yates, J. R., and Wysocki, V. H. Cleavage n-terminal to proline: analysis of a database of peptide tandem mass spectra. Analytical chemistry, 75 0 (9): 0 1963--1971, 2003

    work page 1963

  6. [6]

    Learning to rank using gradient descent

    Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning, pp.\ 89--96, 2005

    work page 2005

  7. [7]

    Burges, C. J. From ranknet to lambdarank to lambdamart: An overview. Learning, 11 0 (23-581): 0 81, 2010

    work page 2010

  8. [8]

    Learning to rank: from pairwise approach to listwise approach

    Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., and Li, H. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning, pp.\ 129--136, 2007

    work page 2007

Show all 68 references
  1. [9]

    Quantitative and in-depth survey of the isotopic abundance distribution errors in shotgun proteomics

    Chang, C., Zhang, J., Xu, C., Zhao, Y., Ma, J., Chen, T., He, F., Xie, H., and Zhu, Y. Quantitative and in-depth survey of the isotopic abundance distribution errors in shotgun proteomics. Analytical chemistry, 88 0 (13): 0 6844--6851, 2016

    2016

  2. [10]

    J., and Cheng, J

    Chen, C., Hou, J., Tanner, J. J., and Cheng, J. Bioinformatics methods for mass spectrometry-based proteomics data analysis. International journal of molecular sciences, 21 0 (8): 0 2873, 2020

    2020

  3. [11]

    pnovo: de novo peptide sequencing and identification using hcd spectra

    Chi, H., Sun, R.-X., Yang, B., Song, C.-Q., Wang, L.-H., Liu, C., Fu, Y., Yuan, Z.-F., Wang, H.-P., He, S.-M., et al. pnovo: de novo peptide sequencing and identification using hcd spectra. Journal of proteome research, 9 0 (5): 0 2713--2724, 2010

    2010

  4. [12]

    pnovo+: de novo peptide sequencing using complementary hcd and etd tandem mass spectra

    Chi, H., Chen, H., He, K., Wu, L., Yang, B., Sun, R.-X., Liu, J., Zeng, W.-F., Song, C.-Q., He, S.-M., et al. pnovo+: de novo peptide sequencing using complementary hcd and etd tandem mass spectra. Journal of proteome research, 12 0 (2): 0 615--625, 2013

    2013

  5. [13]

    Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine

    Chi, H., Liu, C., Yang, H., Zeng, W.-F., Wu, L., Zhou, W.-J., Wang, R.-M., Niu, X.-N., Ding, Y.-H., Zhang, Y., et al. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nature biotechnology, 36 0 (11): 0 1059--1061, 2018

    2018

  6. [14]

    Rethinking attention with performers

    Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al. Rethinking attention with performers. arXiv preprint arXiv:2009.14794, 2020

    2009 arXiv

  7. [15]

    A., Clauser, K

    Dan c \' k, V., Addona, T. A., Clauser, K. R., Vath, J. E., and Pevzner, P. A. De novo peptide sequencing via tandem mass spectrometry. Journal of computational biology, 6 0 (3-4): 0 327--342, 1999

    1999

  8. [16]

    B., Williams, W., Beljouw, S

    Eloff, K., Kalogeropoulos, K., Morell, O., Mabona, A., Jespersen, J. B., Williams, W., Beljouw, S. P. v., Skwark, M., Laustsen, A. H., Brouns, S. J., et al. De novo peptide sequencing with instanovo: Accurate, database-free peptide identification for large scale proteomics exp...

    2023

  9. [17]

    B., Williams, W., van Beljouw, S

    Eloff, K., Kalogeropoulos, K., Morell, O., Mabona, A., Jespersen, J. B., Williams, W., van Beljouw, S. P., Skwark, M., Laustsen, A. H., Brouns, S. J., et al. De novo peptide sequencing with instanovo: Accurate, database-free peptide identification for large scale proteomics ex...

    2023

  10. [18]

    and Doolittle, R

    Feng, D.-F. and Doolittle, R. F. Progressive sequence alignment as a prerequisitetto correct phylogenetic trees. Journal of molecular evolution, 25: 0 351--360, 1987

    1987

  11. [19]

    Deep multimodal networks for m-type star classification with paired spectrum and photometric image

    Gao, J., Chen, J., Wei, J., Jiang, B., and Luo, A.-L. Deep multimodal networks for m-type star classification with paired spectrum and photometric image. Publications of the Astronomical Society of the Pacific, 135 0 (1046): 0 044503, 2023

    2023

  12. [20]

    Rethink training of bert rerankers in multi-stage retrieval pipeline

    Gao, L., Dai, Z., and Callan, J. Rethink training of bert rerankers in multi-stage retrieval pipeline. In Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28--April 1, 2021, Proceedings, Part II 43, pp.\ 280--286. Spri...

    2021

  13. [21]

    Learning-to-rank with bert in tf-ranking

    Han, S., Wang, X., Bendersky, M., and Najork, M. Learning-to-rank with bert in tf-ranking. arXiv preprint arXiv:2004.08476, 2020

    2004 arXiv

  14. [22]

    L., Pan, C., Chourey, K., and Giannone, R

    Hettich, R. L., Pan, C., Chourey, K., and Giannone, R. J. Metaproteomics: harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities. Analytical chemistry, 85 0 (9): 0 4203--4214, 2013

    2013

  15. [23]

    Axial attention in multidimensional transformers

    Ho, J., Kalchbrenner, N., Weissenborn, D., and Salimans, T. Axial attention in multidimensional transformers. arXiv preprint arXiv:1912.12180, 2019

    1912 arXiv

  16. [24]

    Ccnet: Criss-cross attention for semantic segmentation

    Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. Ccnet: Criss-cross attention for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pp.\ 603--612, 2019

    2019

  17. [25]

    Jiang, D., Ren, X., and Lin, B. Y. Llm-blender: Ensembling large language models with pairwise ranking and generative fusion. arXiv preprint arXiv:2306.02561, 2023

    2023 arXiv

  18. [26]

    Contranovo: a contrastive learning approach to enhance de novo peptide sequencing

    Jin, Z., Xu, S., Zhang, X., Ling, T., Dong, N., Ouyang, W., Gao, Z., Chang, C., and Sun, S. Contranovo: a contrastive learning approach to enhance de novo peptide sequencing. arxiv. arXiv preprint arXiv:2312.11584, 2023

    2023 arXiv

  19. [27]

    Contranovo: A contrastive learning approach to enhance de novo peptide sequencing

    Jin, Z., Xu, S., Zhang, X., Ling, T., Dong, N., Ouyang, W., Gao, Z., Chang, C., and Sun, S. Contranovo: A contrastive learning approach to enhance de novo peptide sequencing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp.\ 144--152, 2024

    2024

  20. [28]

    Highly accurate protein structure prediction with alphafold

    Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Z \' dek, A., Potapenko, A., et al. Highly accurate protein structure prediction with alphafold. Nature, 596 0 (7873): 0 583--589, 2021

    2021

  21. [29]

    W., Chuangsuwanich, E., and Sriswasdi, S

    Karunratanakul, K., Tang, H.-Y., Speicher, D. W., Chuangsuwanich, E., and Sriswasdi, S. Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework. Molecular & Cellular Proteomics, 18 0 (12): 0 2478--2491, 2019

    2019

  22. [30]

    Deep learning

    LeCun, Y., Bengio, Y., and Hinton, G. Deep learning. nature, 521 0 (7553): 0 436--444, 2015

    2015

  23. [31]

    V., Valente, R

    Leprevost, F. V., Valente, R. H., Lima, D. B., Perales, J., Melani, R., Yates III, J. R., Barbosa, V. C., Junqueira, M., and Carvalho, P. C. Pepexplorer: a similarity-driven tool for analyzing de novo sequencing results. Molecular & Cellular Proteomics, 13 0 (9): 0 2480--2489, 2014

    2014

  24. [32]

    Accurate de novo peptide sequencing using fully convolutional neural networks

    Liu, K., Ye, Y., Li, S., and Tang, H. Accurate de novo peptide sequencing using fully convolutional neural networks. Nature Communications, 14 0 (1): 0 7974, 2023

    2023

  25. [33]

    Novor: real-time peptide de novo sequencing software

    Ma, B. Novor: real-time peptide de novo sequencing software. Journal of the American Society for Mass Spectrometry, 26 0 (11): 0 1885--1894, 2015

    2015

  26. [34]

    Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry

    Ma, B., Zhang, K., Hendrie, C., Liang, C., Li, M., Doherty-Kirby, A., and Lajoie, G. Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid communications in mass spectrometry, 17 0 (20): 0 2337--2342, 2003

    2003

  27. [35]

    Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model

    Mao, Z., Zhang, R., Xin, L., and Li, M. Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model. Nature Machine Intelligence, 5 0 (11): 0 1250--1260, 2023

    2023

  28. [36]

    E., Diament, B., Frewen, B., Howbert, J

    McIlwain, S., Tamura, K., Kertesz-Farkas, A., Grant, C. E., Diament, B., Frewen, B., Howbert, J. J., Hoopmann, M. R., Kall, L., Eng, J. K., et al. Crux: rapid open source protein tandem mass spectrometry analysis. Journal of proteome research, 13 0 (10): 0 4488--4491, 2014

    2014

  29. [37]

    Muth, T., Hartkopf, F., Vaudel, M., and Renard, B. Y. A potential golden age to come—current tools, recent use cases, and future avenues for de novo sequencing in proteomics. Proteomics, 18 0 (18): 0 1700150, 2018

    2018

  30. [38]

    Needleman, S. B. and Wunsch, C. D. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of molecular biology, 48 0 (3): 0 443--453, 1970

    1970

  31. [39]

    I., Keller, A., Kolker, E., and Aebersold, R

    Nesvizhskii, A. I., Keller, A., Kolker, E., and Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Analytical chemistry, 75 0 (17): 0 4646--4658, 2003

    2003

  32. [40]

    Ng, C. C. A., Zhou, Y., and Yao, Z.-P. Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review. Analytica Chimica Acta, pp.\ 341330, 2023

    2023

  33. [41]

    Multi-stage document ranking with bert

    Nogueira, R., Yang, W., Cho, K., and Lin, J. Multi-stage document ranking with bert. arXiv preprint arXiv:1910.14424, 2019

    1910 arXiv

  34. [42]

    Document ranking with a pretrained sequence-to-sequence model

    Nogueira, R., Jiang, Z., and Lin, J. Document ranking with a pretrained sequence-to-sequence model. arXiv preprint arXiv:2003.06713, 2020

    2003 arXiv

  35. [43]

    Training language models to follow instructions with human feedback

    Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35: 0 27730--27744, 2022

    2022

  36. [44]

    M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., and Rives, A

    Rao, R. M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., and Rives, A. Msa transformer. In International Conference on Machine Learning, pp.\ 8844--8856. PMLR, 2021

    2021

  37. [45]

    X., She, Q., Wu, H., Wang, H., and Wen, J.-R

    Ren, R., Qu, Y., Liu, J., Zhao, W. X., She, Q., Wu, H., Wang, H., and Wen, J.-R. Rocketqav2: A joint training method for dense passage retrieval and passage re-ranking. arXiv preprint arXiv:2110.07367, 2021

    2021 arXiv

  38. [46]

    W., Lam, H., Eng, J

    Shteynberg, D., Deutsch, E. W., Lam, H., Eng, J. K., Sun, Z., Tasman, N., Mendoza, L., Moritz, R. L., Aebersold, R., and Nesvizhskii, A. I. iprophet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimate...

    2011

  39. [47]

    Spivak, M., Weston, J., Bottou, L., Kall, L., and Noble, W. S. Improvements to the percolator algorithm for peptide identification from shotgun proteomics data sets. Journal of proteome research, 8 0 (7): 0 3737--3745, 2009

    2009

  40. [48]

    L., Smith, L

    Tabb, D. L., Smith, L. L., Breci, L. A., Wysocki, V. H., Lin, D., and Yates, J. R. Statistical characterization of ion trap tandem mass spectra from doubly charged tryptic peptides. Analytical chemistry, 75 0 (5): 0 1155--1163, 2003

    2003

  41. [49]

    H., Zhang, X., Xin, L., Shan, B., and Li, M

    Tran, N. H., Zhang, X., Xin, L., Shan, B., and Li, M. De novo peptide sequencing by deep learning. Proceedings of the National Academy of Sciences, 114 0 (31): 0 8247--8252, 2017

    2017

  42. [50]

    Axial-deeplab: Stand-alone axial-attention for panoptic segmentation

    Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., and Chen, L.-C. Axial-deeplab: Stand-alone axial-attention for panoptic segmentation. In European conference on computer vision, pp.\ 108--126. Springer, 2020 a

    2020

  43. [51]

    S., Cha, S

    Wang, M., Wang, J., Carver, J., Pullman, B. S., Cha, S. W., and Bandeira, N. Assembling the community-scale discoverable human proteome. Cell systems, 7 0 (4): 0 412--421, 2018

    2018

  44. [52]

    Z., Khabsa, M., Fang, H., and Ma, H

    Wang, S., Li, B. Z., Khabsa, M., Fang, H., and Ma, H. Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768, 2020 b

    2006 arXiv

  45. [53]

    Biatnovo: A self-attention based bidirectional peptide sequencing method

    Wu, S., Luan, Z., Fu, Z., Wang, Q., and Guo, T. Biatnovo: A self-attention based bidirectional peptide sequencing method. bioRxiv, pp.\ 2023--05, 2023

    2023

  46. [54]

    Listwise approach to learning to rank: theory and algorithm

    Xia, F., Liu, T.-Y., Wang, J., Zhang, W., and Li, H. Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th international conference on Machine learning, pp.\ 1192--1199, 2008

    2008

  47. [55]

    Xia, J., Chen, S., Zhou, J., Ling, T., Du, W., Liu, S., and Li, S. Z. Adanovo: Adaptive emph \ De Novo \ peptide sequencing with conditional mutual information. arXiv preprint arXiv:2403.07013, 2024 a

    2024 arXiv

  48. [56]

    Xia, J., Liu, S., Zhou, J., Chen, S., Xiang, H., Liu, Z., Liu, Y., and Li, S. Z. Bridging the gap between database search and de novo peptide sequencing with searchnovo. bioRxiv, pp.\ 2024--10, 2024 b

    2024

  49. [57]

    pnovo 3: precise de novo peptide sequencing using a learning-to-rank framework

    Yang, H., Chi, H., Zeng, W.-F., Zhou, W.-J., and He, S.-M. pnovo 3: precise de novo peptide sequencing using a learning-to-rank framework. Bioinformatics, 35 0 (14): 0 i183--i190, 2019

    2019

  50. [58]

    Introducing -helixnovo for practical large-scale de novo peptide sequencing

    Yang, T., Ling, T., Sun, B., Liang, Z., Xu, F., Huang, X., Xie, L., He, Y., Li, L., He, F., et al. Introducing -helixnovo for practical large-scale de novo peptide sequencing. Briefings in Bioinformatics, 25 0 (2): 0 bbae021, 2024 a

    2024

  51. [59]

    Introducing -helixnovo for practical large-scale de novo peptide sequencing

    Yang, T., Ling, T., Sun, B., Liang, Z., Xu, F., Huang, X., Xie, L., He, Y., Li, L., He, F., et al. Introducing -helixnovo for practical large-scale de novo peptide sequencing. Briefings in Bioinformatics, 25 0 (2): 0 bbae021, 2024 b

    2024

  52. [60]

    Yilmaz, M., Fondrie, W., Bittremieux, W., Oh, S., and Noble, W. S. De novo mass spectrometry peptide sequencing with a transformer model. In International Conference on Machine Learning, pp.\ 25514--25522. PMLR, 2022

    2022

  53. [61]

    E., Bittremieux, W., Melendez, C

    Yilmaz, M., Fondrie, W. E., Bittremieux, W., Melendez, C. F., Nelson, R., Ananth, V., Oh, S., and Noble, W. S. Sequence-to-sequence translation from mass spectra to peptides with a transformer model. BioRxiv, pp.\ 2023--01, 2023

    2023

  54. [62]

    -primenovo: An accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing

    Zhang, X., Ling, T., Jin, Z., Xu, S., Gao, Z., Sun, B., Qiu, Z., Dong, N., Wang, G., Wang, G., et al. -primenovo: An accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing. bioRxiv, pp.\ 2024--05, 2024

    2024

  55. [63]

    -primenovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing

    Zhang, X., Ling, T., Jin, Z., Xu, S., Gao, Z., Sun, B., Qiu, Z., Wei, J., Dong, N., Wang, G., et al. -primenovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing. Nature Communications, 16 0 (1): 0 267, 2025

    2025

  56. [64]

    Zhou, J., Chen, S., Xia, J., Liu, S., Ling, T., Du, W., Liu, Y., Yin, J., and Li, S. Z. Novobench: Benchmarking deep learning-based de novo peptide sequencing methods in proteomics. arXiv preprint arXiv:2406.11906, 2024

    2024 arXiv

  57. [65]

    pdeep: predicting ms/ms spectra of peptides with deep learning

    Zhou, X.-X., Zeng, W.-F., Chi, H., Luo, C., Liu, C., Zhan, J., He, S.-M., and Zhang, Z. pdeep: predicting ms/ms spectra of peptides with deep learning. Analytical chemistry, 89 0 (23): 0 12690--12697, 2017

    2017

  58. [66]

    Rankt5: Fine-tuning t5 for text ranking with ranking losses

    Zhuang, H., Qin, Z., Jagerman, R., Hui, K., Ma, J., Lu, J., Ni, J., Wang, X., and Bendersky, M. Rankt5: Fine-tuning t5 for text ranking with ranking losses. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.\...

    2023

  59. [67]

    P., Wilhelm, M., Schmidt, T., M \'e dard, G., Zerweck, J., Knaute, T., Wenschuh, H., Reimer, U., Schnatbaum, K., and Kuster, B

    Zolg, D. P., Wilhelm, M., Schmidt, T., M \'e dard, G., Zerweck, J., Knaute, T., Wenschuh, H., Reimer, U., Schnatbaum, K., and Kuster, B. Proteometools: Systematic characterization of 21 post-translational protein modifications by liquid chromatography tandem mass spectrometry ...

    2018

  60. [68]

    and Mann, M

    Zubarev, R. and Mann, M. On the proper use of mass accuracy in proteomics. Molecular & Cellular Proteomics, 6 0 (3): 0 377--381, 2007

    2007

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.