The UTC Graduate School is pleased to announce that Meng Hsiu Tsai will present Doctoral research titled, ADVANCE METABOLITE IDENTIFICATION FROM TANDEM MASS SPECTRA USING DEEP GENERATIVE MODELS on 09/15/2025 at 12:30 pm-2:30 pm at https://tennessee.zoom.us/j/5145534460. Everyone is invited to attend. 

Computational Science

Chair: Yingfeng Wang

Abstract:

Tandem mass spectrometry (MS/MS) is a modern technique for measuring metabolites, a type of small molecules involved in metabolism. MS/MS spectra, the output of MS/MS instruments, represent molecules by the fragment patterns of compounds that contain structural features of the precursor molecules. The database-searching strategy is the most popular for metabolite identification among its peers. It matches the query MS/MS spectrum to a collection of molecule candidates, called a database, and identifies the metabolite associated with the spectrum by selecting the metabolite that best matches the query spectrum. This study uses the database-searching strategy and focuses on developing a novel machine learning identification tool. This tool applies autoencoders to map metabolite structures and MS/MS spectra to latent spaces separately. Then, we train a classifier to identify real metabolite-spectrum matches based on the latent space features of metabolites and spectra. Further, we build a generative adversarial network (GAN) to optimize the classifier as the discriminator. A large number of experiments are conducted. The experimental results verify the effectiveness of our tool.