UFII Fellows Journal Club Seminar Series – Xinsong Du

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UFII Fellows Journal Club Seminar Series – Xinsong Du

April 4 @ 12:00 pm - 1:00 pm

UFII Fellows Journal Club Seminar Series 

Computational Reproducibility in Liquid Chromatography-Mass Spectrometry-Based Clinical Metabolomics Data Processing

by Xinsong Du
Ph.D. Candidate, Department of Health Outcomes & Biomedical Informatics

Monday, April 4, 2022, 12:00PM – 1:00PM via Zoom

ABSTRACT:

Reproducible research is defined as research that enables others to obtain consistent results using the same input data. Biomedical research that is not reproducible risks not only the safety of patients but also wastes scientists’ time to pursue false leads, as well as research funding from agencies, hurts the impact of the research and damages the researcher’s reputation. Clinical metabolomics is the application of the systematic study of small molecules (metabolites) within cells, biofluids, tissues, or organisms, to clinical problems. Clinical metabolomics has emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. Population-level implementation of clinical metabolomics is currently limited by informatics challenges that include reproducible data processing. Liquid chromatography-mass spectrometry (LC-MS) is a data acquisition technique for clinical metabolomics that obtains great popularity due to its high sensitivity. Workflow for LC-MS-based clinical metabolomics includes sample preparation, data acquisition, data processing, and data interpretation. Data processing is the first computational step, and its reproducibility is essential for reliable and rigorous computational data interpretation. Therefore, improving the reproducibility of LC-MS clinical metabolomics data processing has great translational importance. This seminar will discuss challenges and opportunities in the computational reproducibility of LC-MS-based clinical metabolomics data processing.

Bio:

Xinsong Du is a Ph.D. candidate in the Department of Health Outcomes and Biomedical Informatics at the University of Florida (UF), advised by Dr. Dominick Lemas. His research interests are interdisciplinary, including research reproducibility, computational metabolomics, machine learning in healthcare, and biomedical research software development. Before joining his Ph.D. program, Xinsong received his master’s degree in the Department of Electrical and Computer Engineering at UF. His research has been published in several well-recognized journals and conferences. Additionally, he has been serving as a peer reviewer for multiple high-impact journals and conferences in the field of biomedical informatics, such as Journal of Biomedical Informatics. Notably, he received a Certificate of Outstanding Merit from the College of Medicine and International Center during the UF International Education Week in November 2021.

Monday, April 4, 2022
12:00PM – 1:00PM
Via Zoom

Registration Below. Instructions to attend talk via Zoom will be emailed to you.

Details

Date:
April 4
Time:
12:00 pm - 1:00 pm
Event Category: