Deep learning approaches for data-independent acquisition proteomics
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep learning approaches for data-independent acquisition proteomics
Authors
Keywords
-
Journal
Expert Review of Proteomics
Volume 18, Issue 12, Pages 1031-1043
Publisher
Informa UK Limited
Online
2021-12-17
DOI
10.1080/14789450.2021.2020654
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Data-independent acquisition method for ubiquitinome analysis reveals regulation of circadian biology
- (2021) Fynn M. Hansen et al. Nature Communications
- Deep learning the collisional cross sections of the peptide universe from a million experimental values
- (2021) Florian Meier et al. Nature Communications
- pDeep3: Toward More Accurate Spectrum Prediction with Fast Few-Shot Learning
- (2021) Ching Tarn et al. ANALYTICAL CHEMISTRY
- PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only
- (2021) Hao Cheng et al. ANALYTICAL CHEMISTRY
- Sequence-Specific Model for Predicting Peptide Collision Cross Section Values in Proteomic Ion Mobility Spectrometry
- (2021) Chih-Hsiang Chang et al. JOURNAL OF PROTEOME RESEARCH
- MaxDIA enables library-based and library-free data-independent acquisition proteomics
- (2021) Pavel Sinitcyn et al. NATURE BIOTECHNOLOGY
- Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine
- (2021) Qiang Gu et al. PLoS Computational Biology
- Advances and Utility of the Human Plasma Proteome
- (2021) Eric W. Deutsch et al. JOURNAL OF PROTEOME RESEARCH
- DeepLC can predict retention times for peptides that carry as-yet unseen modifications
- (2021) Robbin Bouwmeester et al. NATURE METHODS
- DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation
- (2021) Ronghui Lou et al. Nature Communications
- GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control
- (2021) Yi Yang et al. Nature Communications
- Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics
- (2021) Mingxuan Gao et al. Communications Biology
- Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries
- (2020) Bart Van Puyvelde et al. PROTEOMICS
- In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
- (2020) Yi Yang et al. Nature Communications
- Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries
- (2020) Dorte B. Bekker-Jensen et al. Nature Communications
- Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020
- (2020) Fangfei Zhang et al. PROTEOMICS
- Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis
- (2020) Bo Wen et al. Nature Communications
- Generating high quality libraries for DIA MS with empirically corrected peptide predictions
- (2020) Brian C. Searle et al. Nature Communications
- Using deep neural networks and biological subwords to detect protein S-sulfenylation sites
- (2020) Duyen Thi Do et al. BRIEFINGS IN BIOINFORMATICS
- A Deep Learning‐Based Tumor Classifier Directly Using MS Raw Data
- (2020) Hao Dong et al. PROTEOMICS
- Fragment Mass Spectrum Prediction Facilitates Site Localization of Phosphorylation
- (2020) Yi Yang et al. JOURNAL OF PROTEOME RESEARCH
- Deep learning in proteomics
- (2020) Bo Wen et al. PROTEOMICS
- diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition
- (2020) Florian Meier et al. NATURE METHODS
- The Scope, Functions, and Dynamics of Posttranslational Protein Modifications
- (2019) A. Harvey Millar et al. Annual Review of Plant Biology
- Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning
- (2019) Siegfried Gessulat et al. NATURE METHODS
- High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis
- (2019) Shivani Tiwary et al. NATURE METHODS
- MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning
- (2019) Wen-Feng Zeng et al. ANALYTICAL CHEMISTRY
- AP3: An Advanced Proteotypic Peptide Predictor for Targeted Proteomics by Incorporating Peptide Digestibility
- (2019) Zhiqiang Gao et al. ANALYTICAL CHEMISTRY
- Prediction of LC-MS/MS properties of peptides from sequence by deep learning
- (2019) Shenheng Guan et al. MOLECULAR & CELLULAR PROTEOMICS
- Glyco-DIA: a method for quantitative O-glycoproteomics with in silico-boosted glycopeptide libraries
- (2019) Zilu Ye et al. NATURE METHODS
- DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
- (2019) Vadim Demichev et al. NATURE METHODS
- DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map
- (2019) Fatema Tuz Zohora et al. Scientific Reports
- MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks
- (2019) Yang-Ming Lin et al. BMC GENOMICS
- Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation
- (2019) Nguyen Quoc Khanh Le et al. Frontiers in Physiology
- Trapped ion mobility spectrometry: A short review
- (2018) Mark E. Ridgeway et al. INTERNATIONAL JOURNAL OF MASS SPECTROMETRY
- Opportunities and obstacles for deep learning in biology and medicine
- (2018) Travers Ching et al. Journal of the Royal Society Interface
- Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning
- (2018) Chunwei Ma et al. ANALYTICAL CHEMISTRY
- Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial
- (2018) Christina Ludwig et al. Molecular Systems Biology
- Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
- (2018) Scott M. Lundberg et al. Nature Biomedical Engineering
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry
- (2018) Ngoc Hieu Tran et al. NATURE METHODS
- Clinical potential of mass spectrometry-based proteogenomics
- (2018) Bing Zhang et al. Nature Reviews Clinical Oncology
- Artificial Intelligence Understands Peptide Observability and Assists With Absolute Protein Quantification
- (2018) David Zimmer et al. Frontiers in Plant Science
- Mass spectrometry-based proteomics in cancer research
- (2017) William C. Cho Expert Review of Proteomics
- pSite: Amino Acid Confidence Evaluation for Quality Control of De Novo Peptide Sequencing and Modification Site Localization
- (2017) Hao Yang et al. JOURNAL OF PROTEOME RESEARCH
- Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach
- (2017) Elizabeth Guruceaga et al. JOURNAL OF PROTEOME RESEARCH
- Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses
- (2017) George Rosenberger et al. NATURE METHODS
- De novo peptide sequencing by deep learning
- (2017) Ngoc Hieu Tran et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- PROCAL: A Set of 40 Peptide Standards for Retention Time Indexing, Column Performance Monitoring, and Collision Energy Calibration
- (2017) Daniel Paul Zolg et al. PROTEOMICS
- DeepPep: Deep proteome inference from peptide profiles
- (2017) Minseung Kim et al. PLoS Computational Biology
- Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics
- (2016) John F. Timms et al. Expert Review of Proteomics
- Peptide retention time prediction
- (2016) Luminita Moruz et al. MASS SPECTROMETRY REVIEWS
- A multicenter study benchmarks software tools for label-free proteome quantification
- (2016) Pedro Navarro et al. NATURE BIOTECHNOLOGY
- Plug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry
- (2016) Robert T Lawrence et al. NATURE METHODS
- Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues
- (2015) Roland Bruderer et al. MOLECULAR & CELLULAR PROTEOMICS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files
- (2015) Yuanyue Li et al. NATURE METHODS
- DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics
- (2015) Chih-Chiang Tsou et al. NATURE METHODS
- MS2PIP prediction server: compute and visualize MS2peak intensity predictions for CID and HCD fragmentation
- (2015) Sven Degroeve et al. NUCLEIC ACIDS RESEARCH
- Processing strategies and software solutions for data-independent acquisition in mass spectrometry
- (2015) Aivett Bilbao et al. PROTEOMICS
- Prediction of Peptide Fragment Ion Mass Spectra by Data Mining Techniques
- (2014) Nai-ping Dong et al. ANALYTICAL CHEMISTRY
- Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data
- (2014) Ermir Qeli et al. Journal of Proteomics
- OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data
- (2014) Hannes L Röst et al. NATURE BIOTECHNOLOGY
- IMSPeptider: A computational peptide collision cross-section area calculator based on a novel molecular dynamics simulation protocol
- (2013) Ranieri V. de Carvalho et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Multiplexed MS/MS for improved data-independent acquisition
- (2013) Jarrett D Egertson et al. NATURE METHODS
- Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics
- (2013) Ute Distler et al. NATURE METHODS
- A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies
- (2013) Pratik Jagtap et al. PROTEOMICS
- Application of modern reversed-phase peptide retention prediction algorithms to the Houghten and DeGraw dataset: Peptide helicity and its effect on prediction accuracy
- (2012) Janice Reimer et al. JOURNAL OF CHROMATOGRAPHY A
- MS-Simulator: Predicting Y-Ion Intensities for Peptides with Two Charges Based on the Intensity Ratio of Neighboring Ions
- (2012) Shiwei Sun et al. JOURNAL OF PROTEOME RESEARCH
- Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis
- (2012) Ludovic C. Gillet et al. MOLECULAR & CELLULAR PROTEOMICS
- Using iRT, a normalized retention time for more targeted measurement of peptides
- (2012) Claudia Escher et al. PROTEOMICS
- CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches
- (2011) Claire E. Eyers et al. MOLECULAR & CELLULAR PROTEOMICS
- mProphet: automated data processing and statistical validation for large-scale SRM experiments
- (2011) Lukas Reiter et al. NATURE METHODS
- On the Accuracy and Limits of Peptide Fragmentation Spectrum Prediction
- (2010) Sujun Li et al. ANALYTICAL CHEMISTRY
- Machine learning based prediction for peptide drift times in ion mobility spectrometry
- (2010) Anuj R. Shah et al. BIOINFORMATICS
- Skyline: an open source document editor for creating and analyzing targeted proteomics experiments
- (2010) Brendan MacLean et al. BIOINFORMATICS
- Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics
- (2010) Luminita Moruz et al. JOURNAL OF PROTEOME RESEARCH
- Object Detection with Discriminatively Trained Part-Based Models
- (2009) P F Felzenszwalb et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography−Tandem Mass Spectrometry
- (2009) David L. Tabb et al. JOURNAL OF PROTEOME RESEARCH
- Prediction of high-responding peptides for targeted protein assays by mass spectrometry
- (2009) Vincent A Fusaro et al. NATURE BIOTECHNOLOGY
- Review of factors that influence the abundance of ions produced in a tandem mass spectrometer and statistical methods for discovering these factors
- (2008) Sheila J. Barton et al. MASS SPECTROMETRY REVIEWS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search