ArticleAnalytical chemistry2020
Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network.
Article in Analytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 81 citations in OpenAlex.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Comprehensive top-down mass spectral repository enables pan-dataset analysis and top-down spectral prediction.bioRxiv : the preprint server for biology · 2026Article
- Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage.Nature methods · 2026Article
- Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.Journal of proteome research · 2026Article
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.Nature communications · 2025Article
- FastSpel: A Method for Fast Spectral Library Generation.Journal of proteome research · 2025Article
- DeepSecMS Advances DIA-Based Selenoproteome Profiling Through Cys-to-Sec Proxy Training.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Carafe enables high qualitybioRxiv : the preprint server for biology · 2025Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction.International journal of molecular sciences · 2024Article
- SpecEncoder: deep metric learning for accurate peptide identification in proteomics.Bioinformatics (Oxford, England) · 2024Article
- Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles.Journal of proteome research · 2024Article
- Mass spectra prediction with structural motif-based graph neural networks.Scientific reports · 2024Article
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.Nature communications · 2024Article
- Accurate de novo peptide sequencing using fully convolutional neural networks.Nature communications · 2023Article
- MSBooster: improving peptide identification rates using deep learning-based features.Nature communications · 2023Article
- AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence.Journal of proteome research · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The ability to predict tandem mass (MS/MS) spectra from peptide sequences can significantly enhance our understanding of the peptide fragmentation process and could improve peptide identification in proteomics. However, current approaches for predicting high-energy collisional dissociation (HCD) spectra are limited to predict the intensities of expected ion types, that is, the a/b/c/x/y/z ions and their neutral loss derivatives (referred to as
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.