Korea University · 情報科学
Professor Seoung Bum Kim's research lab specializes in advanced data analytics and machine learning applications in health sciences, chemistry, and pharmaceutical innovation. The lab focuses on developing statistical and computational methods for feature selection, multivariate process monitoring, and intelligent molecular design. Key research directions include the integration of nonparametric statistical techniques like the bootstrap with multivariate control charts, the application of association rule mining and network analysis to traditional medical texts, and the use of deep generative models—particularly generative adversarial networks with reinforcement learning—for de novo drug design. The lab also works on grounding heuristic methods like the Mahalanobis-Taguchi System in rigorous statistical theory to enhance their reliability and interpretability.
Figures are computed from collected data and may differ slightly.
Successful implementation of feature selection in nuclear magnetic resonance (NMR) spectra not only improves classification ability, but also simplifies the entire modeling process and, thus, reduces computational and analytical efforts. Principal component analysis (PCA) and partial least squares (PLS) have been widely used for feature selection in NMR spectra. However, extracting meaningful metabolite features from the reduced dimensions obtained through PCA or PLS is complicated because these
Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop univariate control charts to monitor a single process, no effort has been made to integrate the effectiv
Extracting useful and meaningful patterns from large volumes of text data is of growing importance. In the present study we analyze vast amounts of prescription data, generated from the book of oriental medicine to identify the relationships between the symptoms and the associated medicines used to treat these symptoms. The oriental medicine book used in this study (called Bangyakhappyeon) contains a large number of prescriptions to treat about 54 categorized symptoms and lists the corresponding
Open papers in the app to read, cite, and organize with AI.
