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Dr Michal BASSANI-STERNBERG ¬ Group leader Laboratory Hi-TIDe: Immunopeptidomics Head of clinical Mass spectrometry unit Center of experimental therapeutics Department of oncology UNIL CHUV Ludwig Institute for Cancer Research Lausanne Biopôle 3 Chemin des Boveresses 155, CH-1066 Lausanne (1)
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European Molecular Biology Laboratory 71 Av. des Martyrs, 38000 Grenoble France (1)
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Jens Nielsen, PhD, Dr. Tech. Professor Director of Systems and Synthetic Biology Chief Scientific Officer Novo Nordisk Foundation Center for Biosustainability Systems and Synthetic Biology Chalmers University of Technology Kemivägen 10 SE-412 96 Göteborg SWEDEN (1)
Jožef Stefan Institute, Department of Biochemistry and Molecular and Structural Biology, Jamova cesta 39, 1000 Ljubljana, Slovenia Centre of Excellence for Integrated Approaches in Chemistry and Biology of Proteins (CIPKeBiP), Jamova cesta 39, 1000 Ljubljana, Slovenia (1)
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Max Planck Institute of Biochemistry, Martinsried, Munich, Germany; NNF Center for Protein Research, Copenhagen, Denmark (1)
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Sackler Faculty of Medicine, Tel Aviv University (1)
Systems Biology Ireland, University College Dublin (1)
The Wistar Institute, 3601 Spruce St, Philadelphia, PA 19103, USA (1)
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Classification

bacteria (2)

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4-tetrahydrothiazine-3-carboxylic acid (1)

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Targeted metabolomic profiling and machine learning-based prediction models for persistent atrial fibrillation: a multi-center observational study

Background. Atrial fibrillation (AF), the most common cardiac arrhythmia worldwide, carries a high risk of severe complications. Patients diagnosed with paroxysmal atrial fibrillation (PAF) may progress to persistent atrial fibrillation (PerAF) following a period. We aimed to identify metabo...

2026-07-03 | MTBLS13136 | MetaboLights

Leveraging the Metabolic Fingerprint of Sleep Deprivation and Sleep Restriction for Forensic Applications: A Machine Learning Study in Oral Fluid Metabolomics

As sleep loss leads to accidents and impaired safety, a direct metabolic marker would be beneficial for forensic interpretation.

In a sufficiently powered, randomized, controlled, crossover trial under realistic conditions, we examined the salivary metabolome of 20 young men (habitual slee...

2026-05-21 | MTBLS11413 | MetaboLights

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DDA dataset)

We have developed a new strategy for identifying bacterial species in biological samples using specific LC-MS/MS peptidic signatures. In the first training step, deep proteome coverage of bacteria of interest is obtained in Data Independent Acquisition (DIA) mode, followed by the use of machine lear...

ORGANISM(S): Citrobacter Freundii (ncbitaxon:546) Streptococcus Agalactiae 2603v/r (ncbitaxon:208435) Staphylococcus Aureus Subsp. Aureus Nctc 8325 (ncbitaxon:93061) Streptococcus Mitis (ncbitaxon:28037) Klebsiella Oxytoca (ncbitaxon:571) Staphylococcus Epidermidis Atcc 12228 (ncbitaxon:176280) Enterococcus Faecalis V583 (ncbitaxon:226185) Enterobacter Aerogenes Kctc 2190 (ncbitaxon:1028307) Proteus Mirabilis (ncbitaxon:584) Escherichia Coli K-12 (ncbitaxon:83333) Enterobacter Cloacae (ncbitaxon:550) Klebsiella Pneumoniae Subsp. Pneumoniae Mgh 78578 (ncbitaxon:272620) Pseudomonas Aeruginosa Pao1 (ncbitaxon:208964) Staphylococcus Saprophyticus Subsp. Saprophyticus Atcc 15305 (ncbitaxon:342451) Staphylococcus Haemolyticus Jcsc1435 (ncbitaxon:279808)

2019-05-16 | MSV000083784 | MassIVE

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on MHC-II molecules. The high polymorphism of MHC-II genes represents an important hurdle towards accurate predictions of CD4+ T-cell epitopes in different individuals and different s...

ORGANISM(S): Homo sapiens (Human)

2023-04-07 | PXD034773 | Pride