Paper Citation Record · LEDGER

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.17524.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.17524 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:49:16.243601Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation d76bd2e3-e376-44d4-bac4-c7038637cf6c · outbound

This paper cites and Mann, M.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing and Mann, M

Reference 1

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Observation e3fdb3ab-eb3b-4ab6-8787-ac14e7f495f6 · outbound

This paper cites an unresolved cited work.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 2

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Observation c14da9ec-5d84-4481-aea1-44b4412f0b4a · outbound

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Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 3

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Observation 88aa5cb6-6466-4a4d-ae90-4ddc67977e34 · outbound

This paper cites End-to-end object detection with transformers.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing End-to-end object detection with transformers

Reference 4

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Observation 9e528eef-196c-4607-a74a-06d867ab3d78 · outbound

This paper cites B., Williams, W., van Beljouw, S.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing B., Williams, W., van Beljouw, S

Reference 5

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Observation c1a3416a-6f19-4db3-a47b-56ada067f4ae · outbound

This paper cites Beam Search Strategies for Neural Machine Translation.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Beam Search Strategies for Neural Machine Translation

Reference 6

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Observation 1287a3a5-a127-4f4c-ac84-932d74d77262 · outbound

This paper cites Generative adversarial networks.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Generative adversarial networks

Reference 7

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Observation a18a25d4-c4d8-4513-9f5d-edc0ee6eaba1 · outbound

This paper cites A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models

Reference 8

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Observation c3ca8892-35c7-4064-8249-824c0d697e00 · outbound

This paper cites Contranovo: A contrastive learning approach to enhance de novo peptide sequencing.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Contranovo: A contrastive learning approach to enhance de novo peptide sequencing

Reference 9

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Observation e9619a56-f22e-490a-8254-2639651f513d · outbound

This paper cites Auto-Encoding Variational Bayes.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Auto-Encoding Variational Bayes

Reference 10

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Observation a166db52-81a6-4376-94f0-eaf4385be406 · outbound

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Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 11

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Observation ee6ea643-a4bc-4f57-9993-8fffc37b9772 · outbound

This paper cites Deep learning.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Deep learning

Reference 12

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Observation f66e4330-484f-4b4d-a427-42e7ea0e9883 · outbound

This paper cites Predicting protein function from sequence and structure.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Predicting protein function from sequence and structure

Reference 13

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Observation e2973124-43af-4f68-ba04-87b7b0edb133 · outbound

This paper cites Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets

Reference 14

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Observation 78b9240d-2347-42c5-bdba-eb240dec0e2b · outbound

This paper cites Method for incomplete and imbalanced data based on multivariate imputation by chained equations and ensemble learning.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Method for incomplete and imbalanced data based on multivariate imputation by chained equations and ensemble learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:21.127577Z

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Observation 77ffad5e-1ea6-400c-b11f-9c9851c7eb60 · outbound

This paper cites Relevant applications of generative adversarial networks in drug design and discovery: molecular de novo design, dimensionality reduction, and de novo peptide and protein design.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Relevant applications of generative adversarial networks in drug design and discovery: molecular de novo design, dimensionality reduction, and de novo peptide and protein design

Reference 16

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Observation 4f82dc85-d150-454d-8e8c-3ed6c4ace9d6 · outbound

This paper cites and Tsai, C.-F.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing and Tsai, C.-F

Reference 17

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Observation 86fc52e5-baed-48c0-85bd-f6aff040a712 · outbound

This paper cites Accurate de novo peptide sequencing using fully convolutional neural networks.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Accurate de novo peptide sequencing using fully convolutional neural networks

Reference 18

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Observation 0fe14c56-b4be-4432-8f72-ed7ae90092bc · outbound

This paper cites Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry

Reference 19

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Observation ea8dd4e3-d0b5-4392-9500-f9b7aa5440de · outbound

This paper cites Recent advances in mass spectrometry based clinical proteomics: applications to cancer research.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:20.641828Z

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Observation 2db0e861-4103-4d21-bb66-cda9d6b89e37 · outbound

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

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model

Reference 21

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Observation db148ab5-16c1-4321-bcf9-d52493bd94af · outbound

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Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 22

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Observation 1d7bea82-a2fb-4f4a-bbd5-5f2e6128fa0d · outbound

This paper cites The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:20.329463Z

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Observation 60e1fd56-11fc-4361-b9ba-fd665b9a702f · outbound

This paper cites I., Vitek, O., and Aebersold, R.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing I., Vitek, O., and Aebersold, R

Reference 24

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Observation 6e515701-9787-4ad1-a215-83a41da1e65c · outbound

This paper cites B., Baker, M.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing B., Baker, M

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:19.976107Z

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Observation ec5e8055-3613-4a44-8a4c-8274e980831a · outbound

This paper cites K., Sun, F., White, A.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing K., Sun, F., White, A

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:19.768241Z

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Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 27

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Observation 322f848f-f924-46c8-be42-a276b469b58a · outbound

This paper cites H., Xin, L., Chen, X., Li, M., Shan, B., and Ghodsi, A.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing H., Xin, L., Chen, X., Li, M., Shan, B., and Ghodsi, A

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:19.371323Z

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Observation 1bac66df-30e7-468c-9b82-ff6564323d43 · outbound

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Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 29

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Observation 1e070504-ec04-4c55-aa78-cc807f52fdb1 · outbound

This paper cites T., Bludau, I., Zeng, W.-F., Voytik, E., Ammar, C., Schessner, J.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing T., Bludau, I., Zeng, W.-F., Voytik, E., Ammar, C., Schessner, J

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:18.964237Z

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Observation fa1ae012-c493-4efe-b223-b12688ccf9c6 · outbound

This paper cites Deep learning versus conventional methods for missing data imputation: A review and comparative study.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Deep learning versus conventional methods for missing data imputation: A review and comparative study

Reference 31

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Observation 8523c72f-d1a4-4d45-b58e-6f288013913b · outbound

This paper cites L., Smith, L.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing L., Smith, L

Reference 32

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Observation 022ee5ce-5213-4d0f-b9be-1c8cd9eacd7a · outbound

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

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing H., Zhang, X., Xin, L., Shan, B., and Li, M

Reference 33

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Observation 269d814e-8ec9-44b4-8d7d-2f1610a652ce · outbound

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Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:49:18.613496Z

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Observation 6f39feec-544f-4bae-a3e2-e129846dc85d · outbound

This paper cites Attention is all you need.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Attention is all you need

Reference 35

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Observation e8c8d0a7-5d22-4ca7-b6d8-503c0dba3ff6 · outbound

This paper cites Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:18.402526Z

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Observation 20a64b44-de36-431b-a507-009f03469195 · outbound

This paper cites R., Royston, P., and Wood, A.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing R., Royston, P., and Wood, A

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:18.223006Z

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Observation c42776d8-f2f3-4acf-9e47-978cbdf243fb · outbound

This paper cites A., Washburn, M.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing A., Washburn, M

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:18.041859Z

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Observation 0d7641db-4ab9-460d-b99e-f5285f84d74b · outbound

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Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 39

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Observation 0bb0c092-910c-4f73-9041-6f25c5ed2d4b · outbound

This paper cites Introducing -helixnovo for practical large-scale de novo peptide sequencing.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Introducing -helixnovo for practical large-scale de novo peptide sequencing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:17.511903Z

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Observation c89f80a6-9987-4b75-ac00-1ee3041ee3cc · outbound

This paper cites R., Eng, J.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing R., Eng, J

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:17.298937Z

Source-reported events for the cited work

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Observation 71ab1c7a-5b57-498a-9c19-84cdeb4c3653 · outbound

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Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 42

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Observation a7821713-3578-47b8-8804-b53735f026ab · outbound

This paper cites E., Bittremieux, W., Melendez, C.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing E., Bittremieux, W., Melendez, C

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:17.065586Z

Source-reported events for the cited work

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Observation 1539f654-8e58-4d4b-a3fe-11bb8f86010a · outbound

This paper cites Nearest neighbor selection for iteratively knn imputation.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Nearest neighbor selection for iteratively knn imputation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:49:16.846475Z

Source-reported events for the cited work

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Observation 85e8d5fc-d814-4838-9dd5-5f3e5d698f61 · outbound

This paper cites Diffusion models for missing value imputation in tabular data.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Diffusion models for missing value imputation in tabular data

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:49:16.017247Z

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Observation e8908396-1388-4685-8cb4-eb9830c0ae64 · outbound

This paper cites an unresolved cited work.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:49:16.628975Z

Source-reported events for the cited work

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Observation e51fd283-5f0a-49e7-b751-c78bfabff205 · outbound

This paper cites write newline.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing write newline

Reference 47

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Pith citing papers

No inbound Pith citation observations are available.