The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis
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Citations
Cites
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Self-citationTensorFlow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08695 · Cited by 11,822
Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
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Self-citationThe MaxQuant computational platform for mass spectrometry-based shotgun proteomics
Tyanova, S., Temu, T., & Cox, J. (2016) · Nature Protocols · doi:10.1038/nprot.2016.136 · Cited by 4,797
Self-citationSupport Vector Regression Machines
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. N. (1996) · Cited by 4,658
2016 update of the PRIDE database and its related tools
Vizcaíno, J. A., Csordas, A., del-Toro, N., Dianes, J. A., Griss, J., Lavidas, I., Mayer, G., Perez-Riverol, Y., Reisinger, F., Ternent, T., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1145 · Cited by 3,451
Cited by
The ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics
Deutsch, E. W., Bandeira, N., Sharma, V., Perez-Riverol, Y., Carver, J. J., Kundu, D. J., García-Seisdedos, D., Jarnuczak, A. F., Hewapathirana, S., Pullman, B. S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz984 · Cited by 697
Bekker-Jensen, D. B., Bernhardt, O. M., Hogrebe, A., Martinez-Val, A., Verbeke, L., Gandhi, T., Kelstrup, C. D., Reiter, L., & Olsen, J. V. (2020) · Nature Communications · doi:10.1038/s41467-020-14609-1 · Cited by 411
Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform
Yu, F., Teo, G. C., Kong, A. T., Fröhlich, K., Li, G. X., Demichev, V., & Nesvizhskii, A. I. (2023) · Nature Communications · doi:10.1038/s41467-023-39869-5 · Cited by 292
Pino, L. K., Just, S. C., MacCoss, M. J., & Searle, B. C. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.p119.001913 · Cited by 284
Warinner, C., Korzow Richter, K., & Collins, M. J. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00703 · Cited by 178
Metadata
- Authors
- Tiwary, S., Levy, R., Gutenbrunner, P., Salinas Soto, F., Palaniappan, K. K., Deming, L., Berndl, M., Brant, A., Cimermancic, P., & Cox, J.
- Venue
- Nature Methods
- Published
- 2019-05-27
- Volume
- 16
- Issue
- 6
- Pages
- 519-525
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2945525532
- Cited by
- 256
- References
- 41
- FWCI
- 13.67
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. https://app.cochat.ai/papers/10.1038/s41592-019-0427-6
- The paper
- Tiwary, S., Levy, R., Gutenbrunner, P., Salinas Soto, F., Palaniappan, K. K., Deming, L., Berndl, M., Brant, A., Cimermancic, P., & Cox, J. (2019). High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nature Methods. https://doi.org/10.1038/s41592-019-0427-6
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
