講者：長庚大學人工智慧學系 張書瑋 副教授

時間：2024.4.2 (二) 14:00-16:00

地點：人工智慧研究中心（管理大樓11樓）

線上直播： https://reurl.cc/13D0xD

講者介紹：

Prof. Su-Wei Chang currently serves as an Associated Professor in the Department of Artificial Intelligence, Chang Gung University. She accquired a PhD in Applied mathematics and Statistics from State University of New York, Stony Brook, USA in 2009. She then spent two years doing postdoctoral research in Institute of Biomedical Sciences, Academia Sinica. And she joined Clinical Informatics and Medical Statistics Research Center, Chang Gung University in 2011.  Prof. Su-Wei Chang’s research specialties and directions include the application of statistical methods and artificial intelligence (AI) technologies in clinical and biomedical data, as well as the development of new statistical and AI-assisted analytical methods. Main focus includes: (1) multi-omics data analysis, such as genomics, proteomics, metabolomics data, and bioinformatics analysis in various diseases. (2) integration of omics and clinical data to construct clinical evaluation and disease prediction models. (3) big health data analysis, such as biobanks and health insurance databases through AI and longitudinal statistical analysis methods to develop analytical strategies and evaluation models for precision medicine.  

演講大綱：

Sarcopenia, the age-related loss of muscle mass and function, can significantly impair mobility and independence in older adults and thus increase healthcare costs and aggravate the burden on healthcare systems. While the exact underlying pathogenic mechanisms of sarcopenia are not fully understood, several key factors contribute to its development and progression, such as physical inactivity and poor nutrition, muscle protein metabolism, mitochondrial dysfunction, inflammation and oxidative stress, neuromuscular and hormonal changes, and genetic factors. In this talk, we will share our experience in applying genome-wide association study (GWAS) and statistical approaches to explore whole-genome single nucleotide polymorphisms (SNPs) which are potentially associated with sarcopenia and its related traits, including muscle mass, muscle strength, and physical performance using the data from the Integrating Systematic Data of Geriatric Medicine to Explore the Solution for Health Aging study. In addition, we will discuss how artificial intelligence (AI) can assist in identifying novel targets associated with sarcopenia, uncover hidden patterns, or facilitate the translation of genetic findings through various machine learning or deep learning techniques.

主辦單位：人工智慧研究中心、智慧運算學院

※本活動無需報名。