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A learned embedding for efficient joint analysis of millions of mass spectra
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Probability-based protein identification by searching sequence databases using mass spectrometry data
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Unimod: Protein modifications for mass spectrometry
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Development and validation of a spectral library searching method for peptide identification from MS/MS
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A guided tour of the Trans‐Proteomic Pipeline
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Large‐scale tandem mass spectrum clustering using fast nearest neighbor searching
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Novor: Real-Time Peptide de Novo Sequencing Software
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An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database
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The BioPlex Network: A Systematic Exploration of the Human Interactome
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A Virtual Construction Environment for preconstruction planning
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Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search
inferredcites →https://doi.org/10.1021/acs.analchem.7b02566openalex-snapshot
pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning
inferredcites →https://doi.org/10.1021/acs.analchem.8b02386openalex-snapshot
Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning
inferredcites →https://doi.org/10.1021/acs.analchem.9b04884openalex-snapshot
spectrum_utils : A Python Package for Mass Spectrometry Data Processing and Visualization
inferredcites →https://doi.org/10.1021/acs.jproteome.1c00454openalex-snapshot
ppx: Programmatic Access to Proteomics Data Repositories
inferredcites →https://doi.org/10.1021/acs.jproteome.5b00749openalex-snapshot
MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics
inferredcites →https://doi.org/10.1021/acs.jproteome.6b00239openalex-snapshot
An Alignment-Free “Metapeptide” Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing
inferredcites →https://doi.org/10.1021/acs.jproteome.7b00028openalex-snapshot
Param-Medic: A Tool for Improving MS/MS Database Search Yield by Optimizing Parameter Settings
inferredcites →https://doi.org/10.1021/acs.jproteome.8b00359openalex-snapshot
Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing
inferredcites →https://doi.org/10.1021/acs.jproteome.8b00448openalex-snapshot
msCRUSH: Fast Tandem Mass Spectral Clustering Using Locality Sensitive Hashing
inferredcites →https://doi.org/10.1021/acs.jproteome.8b00717openalex-snapshot
Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework
inferredcites →https://doi.org/10.1021/acs.jproteome.9b00291openalex-snapshot
Extremely Fast and Accurate Open Modification Spectral Library Searching of High-Resolution Mass Spectra Using Feature Hashing and Graphics Processing Units
inferredcites →https://doi.org/10.1021/acs.jproteome.9b00328openalex-snapshot
ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion
inferredcites →https://doi.org/10.1021/pr070361eopenalex-snapshot
Clustering Millions of Tandem Mass Spectra
inferredcites →https://doi.org/10.1021/pr301012jopenalex-snapshot
The State of the Human Proteome in 2012 as Viewed through PeptideAtlas
inferredcites →https://doi.org/10.1021/pr501164ropenalex-snapshot
A Decoy-Free Approach to the Identification of Peptides
inferredcites →https://doi.org/10.1021/pr501173sopenalex-snapshot
Tandem Mass Spectrum Identification via Cascaded Search
inferredcites →https://doi.org/10.1021/pr900947uopenalex-snapshot
Artificial Decoy Spectral Libraries for False Discovery Rate Estimation in Spectral Library Searching in Proteomics
inferredcites →https://doi.org/10.1038/embor.2008.56openalex-snapshot
PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows
inferredcites →https://doi.org/10.1038/nature11234openalex-snapshot
Structure, function and diversity of the healthy human microbiome
inferredcites →https://doi.org/10.1038/ncomms6277openalex-snapshot
MS-GF+ makes progress towards a universal database search tool for proteomics
inferredcites →https://doi.org/10.1038/nmeth.1609openalex-snapshot
Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra
inferredcites →https://doi.org/10.1038/nmeth.2343openalex-snapshot
PRIDE Cluster: building a consensus of proteomics data
inferredcites →https://doi.org/10.1038/nmeth.3902openalex-snapshot
Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets
inferredcites →https://doi.org/10.1038/nmeth.4153openalex-snapshot
Building ProteomeTools based on a complete synthetic human proteome
inferredcites →https://doi.org/10.1038/nmeth1113openalex-snapshot
Semi-supervised learning for peptide identification from shotgun proteomics datasets
inferredcites →https://doi.org/10.1038/s41586-020-2649-2openalex-snapshot
Array programming with NumPy
inferredcites →https://doi.org/10.1038/s41587-019-0391-9openalex-snapshot
Reconstructing kinase network topologies from phosphoproteomics data reveals cancer-associated rewiring
inferredcites →https://doi.org/10.1038/s41592-018-0260-3openalex-snapshot
Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry
inferredcites →https://doi.org/10.1038/s41592-019-0426-7openalex-snapshot
Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning
inferredcites →https://doi.org/10.1038/s41592-019-0427-6openalex-snapshot
High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis
inferredcites →https://doi.org/10.1038/s41592-019-0686-2openalex-snapshot
SciPy 1.0: fundamental algorithms for scientific computing in Python
inferredcites →https://doi.org/10.1073/pnas.1705691114openalex-snapshot
De novo peptide sequencing by deep learning
inferredcites →https://doi.org/10.1074/mcp.m111.014381openalex-snapshot
The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results
inferredcites →https://doi.org/10.1074/mcp.m800192-mcp200openalex-snapshot
MRMaid, the Web-based Tool for Designing Multiple Reaction Monitoring (MRM) Transitions
inferredcites →https://doi.org/10.1074/mcp.o113.030189openalex-snapshot
Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry
inferredcites →https://doi.org/10.1074/mcp.o113.036681openalex-snapshot
The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience
inferredcites →https://doi.org/10.1074/mcp.r110.000133openalex-snapshot
mzML—a Community Standard for Mass Spectrometry Data
inferredcites →https://doi.org/10.1093/bioinformatics/bti383openalex-snapshot
A novel approach for clustering proteomics data using Bayesian fast Fourier transform
inferredcites →https://doi.org/10.1093/bioinformatics/btq140openalex-snapshot
A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval
inferredcites →https://doi.org/10.1093/bioinformatics/btt544openalex-snapshot
MS2PIP: a tool for MS/MS peak intensity prediction
inferredcites →https://doi.org/10.1093/database/bav120openalex-snapshot
The UniProtKB guide to the human proteome
inferredcites →https://doi.org/10.1093/nar/gkv542openalex-snapshot
MS 2 PIP prediction server: compute and visualize MS 2 peak intensity predictions for CID and HCD fragmentation
inferredcites →https://doi.org/10.1093/nar/gky1106openalex-snapshot
The PRIDE database and related tools and resources in 2019: improving support for quantification data
inferredcites →https://doi.org/10.1109/cvpr.2006.100openalex-snapshot
Dimensionality Reduction by Learning an Invariant Mapping
inferredcites →https://doi.org/10.1109/cvpr.2015.7298885openalex-snapshot
Learning from massive noisy labeled data for image classification
inferredcites →https://doi.org/10.1109/mcse.2007.55openalex-snapshot
Matplotlib: A 2D Graphics Environment
inferredcites →https://doi.org/10.1109/mis.2009.36openalex-snapshot
The Unreasonable Effectiveness of Data
inferredcites →https://doi.org/10.1109/tbdata.2019.2921572openalex-snapshot
Billion-Scale Similarity Search with GPUs
inferredcites →https://doi.org/10.1186/1477-5956-4-18openalex-snapshot
Analytical model of peptide mass cluster centres with applications.
inferredcites →https://doi.org/10.1214/009053607000000677openalex-snapshot
Kernel methods in machine learning
inferredcites →https://doi.org/10.1371/journal.pcbi.1001047openalex-snapshot
Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding
inferredcites →https://doi.org/10.1524/auto.2011.0951openalex-snapshot
Simulation physiologischer Regelkreise mit der objektorientierten Modellbibliothek “HumanLib”
inferredcites →https://doi.org/10.1609/aaai.v32i1.11996openalex-snapshot
StarSpace: Embed All The Things!
inferredcites →https://doi.org/10.17863/cam.64134openalex-snapshot
SciPy 1.0: fundamental algorithms for scientific computing in Python
inferredcites →https://doi.org/10.18637/jss.v053.i09openalex-snapshot
fastcluster : Fast Hierarchical, Agglomerative Clustering Routines for R and Python
inferredcites →https://doi.org/10.25080/majora-7b98e3ed-01eopenalex-snapshot
Proceedings of the 14th Python in Science Conference
inferredcites →https://doi.org/10.3115/1073012.1073017openalex-snapshot
Scaling to very very large corpora for natural language disambiguation
inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot
AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
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Efficient Estimation of Word Representations in Vector Space
inferredcites →https://doi.org/10.48550/arxiv.1501.00537openalex-snapshot
A theoretical foundation of the target-decoy search strategy for false discovery rate control in proteomics
inferredcites →https://doi.org/10.48550/arxiv.1710.02765openalex-snapshot
Protein identification with deep learning: from abc to xyz
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/mas.21919openalex-snapshot
Deep Learning Methods for De Novo Peptide Sequencing
inferred← citeshttps://doi.org/10.1002/rcm.9153openalex-snapshot
Large‐scale tandem mass spectrum clustering using fast nearest neighbor searching
inferred← citeshttps://doi.org/10.1007/s11306-022-01963-yopenalex-snapshot
Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools
inferred← citeshttps://doi.org/10.1016/j.aca.2023.341793openalex-snapshot
SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification
inferred← citeshttps://doi.org/10.1016/j.jprot.2020.104070openalex-snapshot
Deep learning embedder method and tool for mass spectra similarity search
inferred← citeshttps://doi.org/10.1021/acs.analchem.9b04884openalex-snapshot
spectrum_utils : A Python Package for Mass Spectrometry Data Processing and Visualization
inferred← citeshttps://doi.org/10.1038/s42256-022-00467-7openalex-snapshot
Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides
inferred← citeshttps://doi.org/10.1109/aemcse50948.2020.00116openalex-snapshot
An Efficient Faiss-Based Search Method for Mass Spectral Library Searching
inferred← citeshttps://doi.org/10.1186/s13321-021-00558-4openalex-snapshot
MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra
inferred← citeshttps://doi.org/10.1371/journal.pone.0259349openalex-snapshot
SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions
inferred← citeshttps://doi.org/10.1609/aaai.v38i1.27765openalex-snapshot
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
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