Aivett Bilbao

The Everything Bagel feature finder automates metabolomics data processing, achieving comparable or better performance than existing methods with significantly reduced computational time.

The paper develops a computationally efficient, library-free DIA workflow for metaproteomics, improving throughput and reducing reliance on spectral libraries.

The paper introduces an LLM-driven pipeline for detecting and structurally modeling post-translational modifications using proteomics data.

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