ArticleBMC genomics2019
MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
17 citing papers in PubMed, 31 citations in OpenAlex.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- 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
- Met-ID: An Open-Source Software for Comprehensive Annotation of Multiple On-Tissue Chemical Modifications in MALDI-MSI.Analytical chemistry · 2025Article
- PyViscount: Validating False Discovery Rate Estimation Methods via Random Search Space Partition.Journal of proteome research · 2025Article
- Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers.Expert review of proteomics · 2024Review
- ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction.International journal of molecular sciences · 2024Article
- Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles.Journal of proteome research · 2024Article
- AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence.Journal of proteome research · 2023Article
- An end-to-end deep learning framework for translating mass spectra to de-novo molecules.Communications chemistry · 2023Article
- Cracking the genetic code with neural networks.Frontiers in artificial intelligence · 2023Article
- Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.PLoS computational biology · 2022Article
- High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry Data.Nature computational science · 2021Article
- Deep learning neural network tools for proteomics.Cell reports methods · 2021Review
- Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments.Analytical chemistry · 2021Article
- Ion Mobility Coupled to a Time-of-Flight Mass Analyzer Combined With Fragment Intensity Predictions Improves Identification of Classical Bioactive Peptides and Small Open Reading Frame-Encoded Peptides.Frontiers in cell and developmental biology · 2021Article
- Deep Learning in Proteomics.Proteomics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database search but is limited to peptides that have been previously identified. An accurate tandem mass spectrum prediction tool is thus crucial in expanding the peptide space and increasing the coverage of spectral library search.
resultsWe propose MS
conclusionsWe showed that MS
Indexed as
Identifiers
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.