相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Deep learning meets metabolomics: a methodological perspective
Partho Sen et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Fragment Mass Spectrum Prediction Facilitates Site Localization of Phosphorylation
Yi Yang et al.
JOURNAL OF PROTEOME RESEARCH (2021)
Urinary proteome profiling for stratifying patients with familial Parkinson's disease
Sebastian Virreira Winter et al.
EMBO MOLECULAR MEDICINE (2021)
Ethical Principles, Constraints, and Opportunities in Clinical Proteomics
Sebastian Porsdam Mann et al.
MOLECULAR & CELLULAR PROTEOMICS (2021)
Deep learning the collisional cross sections of the peptide universe from a million experimental values
Florian Meier et al.
NATURE COMMUNICATIONS (2021)
pDeep3: Toward More Accurate Spectrum Prediction with Fast Few-Shot Learning
Ching Tarn et al.
ANALYTICAL CHEMISTRY (2021)
DeepDigest: Prediction of Protein Proteolytic Digestion with Deep Learning
Jinghan Yang et al.
ANALYTICAL CHEMISTRY (2021)
Plasma Proteomes Can Be Reidentifiable and Potentially Contain Personally Sensitive and Incidental Findings
Philipp E. Geyer et al.
MOLECULAR & CELLULAR PROTEOMICS (2021)
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
Vadim Demichev et al.
NATURE METHODS (2020)
Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network
Kaiyuan Liu et al.
ANALYTICAL CHEMISTRY (2020)
In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics
Yi Yang et al.
NATURE COMMUNICATIONS (2020)
How Machine Learning Will Transform Biomedicine
Jeremy Goecks et al.
CELL (2020)
Proteomic and interactomic insights into the molecular basis of cell functional diversity
Isabell Bludau et al.
NATURE REVIEWS MOLECULAR CELL BIOLOGY (2020)
Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics
Mi Yang et al.
CELL SYSTEMS (2020)
Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling
Tea Dodig-Crnkovic et al.
EBIOMEDICINE (2020)
Combining lipidomics and machine learning to measure clinical lipids in dried blood spots
Stuart G. Snowden et al.
METABOLOMICS (2020)
Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease
Jakob M. Bader et al.
MOLECULAR SYSTEMS BIOLOGY (2020)
The proteome landscape of the kingdoms of life
Johannes B. Mueller et al.
NATURE (2020)
Responsible, practical genomic data sharing that accelerates research
James Brian Byrd et al.
NATURE REVIEWS GENETICS (2020)
A Comprehensive Evaluation of MS/MS Spectrum Prediction Tools for Shotgun Proteomics
Rui Xu et al.
PROTEOMICS (2020)
Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
Matthew The et al.
NATURE COMMUNICATIONS (2020)
Adipose tissue morphology, imaging and metabolomics predicting cardiometabolic risk and family history of type 2 diabetes in non-obese men
Aidin Rawshani et al.
SCIENTIFIC REPORTS (2020)
DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics
Kai Li et al.
PROTEOMICS (2020)
Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease
Lenora Higginbotham et al.
SCIENCE ADVANCES (2020)
Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability
Sung Yang Ho et al.
PATTERNS (2020)
The future of digital health with federated learning
Nicola Rieke et al.
NPJ DIGITAL MEDICINE (2020)
Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Yu Fu et al.
NATURE CANCER (2020)
Data protection, scientific research, and the role of information
Rossana Ducato
COMPUTER LAW & SECURITY REVIEW (2020)
Deep representation learning of electronic health records to unlock patient stratification at scale
Isotta Landi et al.
NPJ DIGITAL MEDICINE (2020)
Deep metabolome: Applications of deep learning in metabolomics
Yotsawat Pomyen et al.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2020)
Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning
Siegfried Gessulat et al.
NATURE METHODS (2019)
High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis
Shivani Tiwary et al.
NATURE METHODS (2019)
MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning
Wen-Feng Zeng et al.
ANALYTICAL CHEMISTRY (2019)
scGen predicts single-cell perturbation responses
Mohammad Lotfollahi et al.
NATURE METHODS (2019)
Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies
Philipp E. Geyer et al.
EMBO MOLECULAR MEDICINE (2019)
Data privacy in the age of personal genomics
Dennis Grishin et al.
NATURE BIOTECHNOLOGY (2019)
High-performance medicine: the convergence of human and artificial intelligence
Eric J. Topol
NATURE MEDICINE (2019)
The PRIDE database and related tools and resources in 2019: improving support for quantification data
Yasset Perez-Riverol et al.
NUCLEIC ACIDS RESEARCH (2019)
Proteoforms as the next proteomics currency
Lloyd M. Smith et al.
SCIENCE (2018)
Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning
Chunwei Ma et al.
ANALYTICAL CHEMISTRY (2018)
Assembling the Community-Scale Discoverable Human Proteome
Mingxun Wang et al.
CELL SYSTEMS (2018)
Plasma Proteome Profiling Reveals Dynamics of Inflammatory and Lipid Homeostasis Markers after Roux-En-Y Gastric Bypass Surgery
Nicolai J. Wewer Albrechtsen et al.
CELL SYSTEMS (2018)
PEPTIDE RETENTION TIME PREDICTION
Luminita Moruz et al.
MASS SPECTROMETRY REVIEWS (2017)
Revisiting biomarker discovery by plasma proteomics
Philipp E. Geyer et al.
MOLECULAR SYSTEMS BIOLOGY (2017)
De novo peptide sequencing by deep learning
Ngoc Hieu Tran et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2017)
MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction
Duolin Wang et al.
BIOINFORMATICS (2017)
pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning
Xie-Xuan Zhou et al.
ANALYTICAL CHEMISTRY (2017)
Proteomics reveals the effects of sustained weight loss on the human plasma proteome
Philipp E. Geyer et al.
MOLECULAR SYSTEMS BIOLOGY (2016)
Mass-spectrometric exploration of proteome structure and function
Ruedi Aebersold et al.
NATURE (2016)
Comment: The FAIR Guiding Principles for scientific data management and stewardship
Mark D. Wilkinson et al.
SCIENTIFIC DATA (2016)
Fast and Accurate Database Searches with MS-GF plus Percolator
Viktor Granholm et al.
JOURNAL OF PROTEOME RESEARCH (2014)
MS-GF plus makes progress towards a universal database search tool for proteomics
Sangtae Kim et al.
NATURE COMMUNICATIONS (2014)
MS2PIP: a tool for MS/MS peak intensity prediction
Sven Degroeve et al.
BIOINFORMATICS (2013)
MS-Simulator: Predicting Y-Ion Intensities for Peptides with Two Charges Based on the Intensity Ratio of Neighboring Ions
Shiwei Sun et al.
JOURNAL OF PROTEOME RESEARCH (2012)
Text-mining solutions for biomedical research: enabling integrative biology
Dietrich Rebholz-Schuhmann et al.
NATURE REVIEWS GENETICS (2012)
Mining electronic health records: towards better research applications and clinical care
Peter B. Jensen et al.
NATURE REVIEWS GENETICS (2012)
Modeling the Cell Cycle: Why Do Certain Circuits Oscillate?
James E. Ferrell et al.
CELL (2011)
Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor-Based Approach The Euro Heart Survey on Atrial Fibrillation
Gregory Y. H. Lip et al.
CHEST (2010)
Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics
Luminita Moruz et al.
JOURNAL OF PROTEOME RESEARCH (2010)
Spectral probabilities and generating functions of tandem mass spectra: A strike against decoy databases
Sangtae Kim et al.
JOURNAL OF PROTEOME RESEARCH (2008)
Minimum Reporting Guidelines for Proteomics Released by the Proteomics Standards Initiative
Andrew R. Jones et al.
MOLECULAR & CELLULAR PROTEOMICS (2008)
Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics
Nico Pfeifer et al.
BMC BIOINFORMATICS (2007)
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Lukas Kall et al.
NATURE METHODS (2007)
Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry
Joshua E. Elias et al.
NATURE METHODS (2007)
Literature mining for the biologist: from information retrieval to biological discovery
LJ Jensen et al.
NATURE REVIEWS GENETICS (2006)
Intensity-based protein identification by machine learning from a library of tandem mass spectra
JE Elias et al.
NATURE BIOTECHNOLOGY (2004)
Prediction of low-energy collision-induced dissociation spectra of peptides
ZQ Zhang
ANALYTICAL CHEMISTRY (2004)
Mass spectrometry-based proteomics
R Aebersold et al.
NATURE (2003)
分享论文
