Committee Chair

Wang, Yingfeng

Committee Member

Liang, Yu; Jain, Hemant (Hemant K.); Qin, Hong; Wu, Dalei

Department

Dept. of Computer Science and Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

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.

Acknowledgments

I would like to express my heartfelt gratitude to my supervisor, Dr. Yingfeng Wang, his unwavering support, insightful guidance, and invaluable feedback throughout the course of my research. His expertise and mentorship have been instrumental in shaping this dissertation. I would also like to express my heartfelt gratitude to my parents, Gang-Hwang and Li-Chen, for their unwavering support and encouragement throughout my academic journey. Your love and belief in me have been a constant source of motivation.

Degree

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.

Date

12-2025

Subject

Deep learning (Machine learning); Generative adversarial networks (Computer networks); Metabolites--Identification; Tandem mass spectrometry

Keyword

Machine Learning, Autoencoders, GAN, Metabolite Identification

Document Type

Doctoral dissertations

DCMI Type

Text

Extent

xiv, 88 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by-sa/4.0/

Date Available

1-1-2027

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Available for download on Friday, January 01, 2027

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