A transformer architecture for retention time prediction in liquid chromatography mass spectrometry‐based proteomics

Tumor drug concentration and phosphoproteomic profiles after two weeks of treatment with sunitinib in patients with newly-diagnosed glioblastoma

(2022) Myra E. van Linde et al.   CLINICAL CANCER RESEARCH

Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities

(2022) Markus Ekvall et al.   JOURNAL OF PROTEOME RESEARCH

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics

(2022) Wen-Feng Zeng et al.   Nature Communications

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

(2021) Alexander Rives et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

MaxDIA enables library-based and library-free data-independent acquisition proteomics

(2021) Pavel Sinitcyn et al.   NATURE BIOTECHNOLOGY

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

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics

(2020) Yi Yang et al.   Nature Communications

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

(2020) Bo Wen et al.   Nature Communications

The Global Phosphorylation Landscape of SARS-CoV-2 Infection

(2020) Mehdi Bouhaddou et al.   CELL

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

Prediction of LC-MS/MS properties of peptides from sequence by deep learning

(2019) Shenheng Guan et al.   MOLECULAR & CELLULAR PROTEOMICS

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

(2019) Vadim Demichev et al.   NATURE METHODS

R2‐P2 rapid‐robotic phosphoproteomics enables multidimensional cell signaling studies

(2019) Mario Leutert et al.   Molecular Systems Biology

CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction

(2018) Viktoria Dorfer et al.   JOURNAL OF PROTEOME RESEARCH

BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes

(2018) Florian Meier et al.   NATURE METHODS

In vivo brain GPCR signaling elucidated by phosphoproteomics

(2018) Jeffrey J. Liu et al.   SCIENCE

Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results

(2017) Roland Bruderer et al.   MOLECULAR & CELLULAR PROTEOMICS

Uncertainty estimation of predictions of peptides’ chromatographic retention times in shotgun proteomics

(2016) Heydar Maboudi Afkham et al.   BIOINFORMATICS

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

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification

(2008) Jürgen Cox et al.   NATURE BIOTECHNOLOGY