{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2212"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2212","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Advance metabolite identification from tandem mass spectra using deep generative models","abstract":"Tandem mass spectrometry (MS/MS) is a modern technique for measuring metabolites. MS/MS spectra 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 database of molecule candidates, identifying 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.","abstract_html":"Tandem mass spectrometry (MS/MS) is a modern technique for measuring metabolites. MS/MS spectra 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 database of molecule candidates, identifying 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.","abstract_has_math":false,"creators":["Tsai, Meng Hsiu"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wang, Yingfeng","Liang, Yu; Jain, Hemant (Hemant K.); Qin, Hong; Wu, Dalei","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2027,"date_issued":"2027-01-01T08:00:00Z","date_published":"2027-01-01T08:00:00Z","updated_at":"2026-07-24T05:47:28Z","subjects":["Deep learning (Machine learning)","Generative adversarial networks (Computer networks)","Metabolites--Identification","Tandem mass spectrometry"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/1030","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Yingfeng","Liang, Yu; Jain, Hemant (Hemant K.); Qin, Hong; Wu, Dalei","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Tsai, Meng Hsiu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12-01T08:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2027-01-01T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral dissertations","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning (Machine learning)","Generative adversarial networks (Computer networks)","Metabolites--Identification","Tandem mass spectrometry"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/1030"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computer Science and Engineering","Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."]},{"key":"dc:description.abstract","label":"Abstract","values":["Tandem mass spectrometry (MS/MS) is a modern technique for measuring metabolites. MS/MS spectra 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 database of molecule candidates, identifying 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."]},{"key":"dc:title","label":"Title","values":["Advance metabolite identification from tandem mass spectra using deep generative models"]}]}],"canonical_facts":{"dc:contributor":["Wang, Yingfeng","Liang, Yu; Jain, Hemant (Hemant K.); Qin, Hong; Wu, Dalei","College of Engineering and Computer Science"],"dc:creator":["Tsai, Meng Hsiu"],"dc:date":["2025-12-01T08:00:00Z"],"dc:date.available":["2027-01-01T08:00:00Z"],"dc:description":["Dept. of Computer Science and Engineering","Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."],"dc:description.abstract":["Tandem mass spectrometry (MS/MS) is a modern technique for measuring metabolites. MS/MS spectra 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 database of molecule candidates, identifying 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."],"dc:identifier":["https://scholar.utc.edu/theses/1030"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Deep learning (Machine learning)","Generative adversarial networks (Computer networks)","Metabolites--Identification","Tandem mass spectrometry"],"dc:title":["Advance metabolite identification from tandem mass spectra using deep generative models"],"dc:type":["Doctoral dissertations","Text"]},"updated_at":"2026-07-24T05:47:28Z"}