Andy Lin

The paper evaluates the impact of methodological differences on splicing neoepitope prediction, finding high variability and limited reproducibility across pipelines.

The paper detects human genetically variant peptides in non-human samples using proteomics data reanalysis.

This paper introduces molecular hypernetworks to improve annotation in metabolomics data by representing complex relationships beyond traditional molecular networks.

Crema is an open-source Python tool that implements target-decoy competition methods for estimating false discovery rates in proteomics experiments.

The paper develops neural networks to predict peptide similarity between mass spectrometry runs using only MS1 data, outperforming baselines and enabling sample characterization.

MS1Connect measures similarity between mass spectrometry runs using MS1 scans, outperforming baselines in species prediction and correlating with MS2 scan similarities.

The paper summarizes recent developments in the Crux toolkit for analyzing bottom-up proteomics data, including speedups, new features, and tools.

The paper improves peptide-level mass spectrometry analysis by showing that double competition (PSM-and-peptide) outperforms single competition (PSM-only) in discovering peptides at a 1% FDR threshold.

The paper introduces a new method to control the false discovery rate in proteomics when only a subset of peptides are relevant, showing it outperforms existing methods.

The paper introduces a well-calibrated score function for high-resolution MS2 data that improves statistical power and outperforms existing search engines.