Carolaine da Silva de Araujo

The paper introduces a conversational framework using an LLM agent to interrogate proteomics data sets, identifying issues in four LC-MS/MS datasets and demonstrating its feasibility in reanalyzing data.

The paper compares sample preparation workflows and homology-driven search strategies to characterize the proteome of a marine sponge, finding S-Trap with homology layers provides the most comprehensive and consistent results.

The paper compares proteomic complexity across human neural models, finding increased molecular complexity in tissue-like models that better reflect in vivo brain biology.