Alireza Nameni

Evaluating the use of non-linear models in data-driven rescoring of peptide-spectrum matches

The paper evaluates non-linear models for peptide-spectrum match rescoring in proteomics, finding that complex models increase PSMs but introduce biased FDR estimates.

Predicting and Elucidating Peptide Retention Mechanisms with Graph Attention Networks

The paper introduces a deep learning model to predict peptide retention times in liquid chromatography and identifies key factors influencing retention through saliency mapping.

iDeepLC: chemical structure information yields improved retention time prediction of peptides with unseen modifications

iDeepLC improves retention time prediction for peptides with unseen modifications by incorporating chemical structural information, outperforming previous models.

ProteoBench: the community-curated platform for comparing proteomics data analysis workflows

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

Collisional cross-section prediction for multiconformational peptide ions with IM2Deep

The paper improves IM2Deep to predict collisional cross-sections for peptides with multiple conformations, using a comprehensive dataset and tailored architecture.

DeepLC introduces transfer learning for accurate LC retention time prediction and adaptation to substantially different modifications and setups

DeepLC uses transfer learning to improve LC retention time prediction across different experimental setups and peptide modifications.