Tine Claeys

OmicsFM is a proteomics foundation model that matches transcriptomic models in performance, revealing complementary biology between proteomics and transcriptomics.

This paper demonstrates that using ionbot with OpenProt improves detection of non-canonical proteins by reducing FDR and increasing reproducibility compared to TPP.

This study establishes quality control standards for single-cell proteomics through inter-laboratory benchmarking, identifying software and technical factors affecting data reliability.

InstaNovo-P improves phosphorylated peptide detection and site localization in phosphoproteomics by using a transformer-based model fine-tuned on phosphoproteomics data.

MLMarker computes tissue similarity scores for proteomics data, identifies tissue origins in biofluids, and provides interpretable predictions using a Random Forest model.

ProteoBench provides a centralized platform for comparing proteomics data analysis workflows, enabling benchmarking and community collaboration.

This study benchmarks eight de novo peptide sequencing models on proteomics datasets to evaluate their performance and identify limitations in resolving sequence ambiguity.

A machine learning model trained on proteomics data from 183 human datasets identifies tissue and cell-type specific proteins with high accuracy.