Ceder Dens

Zero-shot retention time prediction for unseen post-translational modifications with molecular structure encodings

The paper introduces MoSTERT and MoSTERT-2S, transformer-based models for zero-shot retention time prediction of peptides with unseen post-translational modifications, achieving state-of-the-art performance on an external dataset.

Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics

This paper discusses machine learning strategies to overcome data challenges in mass spectrometry-based proteomics, emphasizing the need for standardized datasets and larger data sizes to improve model performance.

A cross-attention transformer encoder for paired sequence data

The paper introduces a cross-attention transformer encoder for multi-sequence inputs and evaluates it on peptide-MHCII binding prediction.

The pitfalls of negative data bias for the T-cell epitope specificity challenge

The paper shows that biased negative data in TCR-epitope binding prediction leads to poor performance in realistic scenarios.

Interpretable deep learning to uncover the molecular binding patterns determining TCR-epitope interactions

This study uses interpretable deep learning to analyze TCR-epitope interactions and identify molecular binding patterns that influence TCR affinity.