Roger Giné

This paper introduces a method using solid ionic matrices with low-temperature evaporation to improve MALDI-MSI sensitivity and resolution for small metabolites, achieving detection of 73 metabolites in mouse brain.

ChemEmbed improves metabolite identification by combining enhanced MS/MS data with multidimensional molecular embeddings, achieving high accuracy in benchmark tests.

SingleFrag predicts MS/MS fragments and annotates metabolites using deep learning, outperforming existing tools and identifying three unknown compounds in human samples.

This paper introduces low-temperature thermal evaporation (LTE) for matrix deposition in MALDI-MSI, showing improved image quality, purity, and stability compared to traditional methods.

HERMES improves metabolite identification and structural annotation in mass spectrometry by using molecular formulas and avoiding peak detection.