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Articles by Robbe Devreese
Transfer learning in DeepLC improves LC retention time prediction across substantially different modifications and setups - Nature Communications
Abstract While LC retention time prediction of peptides and their modifications has proven useful, widespread adoption and optimal performance are hindered by variations in experimental parameters. These variations can render retention time prediction models inaccurate and dramatically reduce the value of predictions for identification, validation, and DIA spectral library generation.
Koina: Democratizing machine learning for proteomics research - Nature Communications
Abstract Recent developments in machine learning (ML) and deep learning have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML models are regularly published, the rate at which the community adopts these models is slow.
Collisional Cross-Section Prediction for Multiconformational Peptide Ions with IM2Deep
Introduction Click to copy section linkSection link copied! Recent advances in LC–IM–MS/MS technology have strongly enhanced identification capabilities in complex proteomics workflows. (1−3) Traditionally, LC–MS/MS systems solely depended on liquid chromatography and mass analyzers for peptide separation and selection before fragmentation.
Collisional cross-section prediction for multiconformational peptide ions with IM2Deep
Abstract Peptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysis of proteomics data, for example for identification or quantification through feature finding. Here, we present an improved version of IM2Deep that is trained on a carefully curated dataset to predict CCS values of multiconformational peptides.
TIMS2Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore
Introduction Click to copy section linkSection link copied! Proteomics is an invaluable tool for understanding human biology, facilitating the discovery of new disease biomarkers, identifying potential therapeutic targets, and exploring interactions with pathogens or microorganisms. (1−4) Various proteomics subfields have emerged that address specific challenges.
TIMS2Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore
Introduction Click to copy section linkSection link copied! Proteomics is an invaluable tool for understanding human biology, facilitating the discovery of new disease biomarkers, identifying potential therapeutic targets, and exploring interactions with pathogens or microorganisms. (1−4) Various proteomics subfields have emerged that address specific challenges.
TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore
Abstract The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields - such as plasma proteomics, immunopeptidomics, and metaproteomics - must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments.
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