Jong Min Lee
Hanyang University · 医学
研究室紹介
Professor Jong Min Lee's research lab specializes in systems biology and computational systems engineering, focusing on the integration of metabolic, signaling, and regulatory networks to understand cellular behavior. The lab develops advanced computational frameworks such as integrated dynamic Flux Balance Analysis (idFBA) to model dynamic cellular phenotypes and predict responses to perturbations. A key focus is on applying these models to disease mechanisms, particularly those involving oxidative stress and cancer, and exploring the therapeutic potential of natural compounds like Ganoderma lucidum. The lab also pioneers microfluidic technologies for scalable, high-throughput generation of 3D cell spheroids to improve in vitro disease modeling and drug screening.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Reactive oxygen species (ROS), such as superoxide anions and hydroxyl radicals, are associated with carcinogenesis and other pathophysiological conditions. Therefore, elimination or inactivation of ROS or inhibition of their excess generation may be beneficial in terms of reducing the risk for cancer and other diseases. Ganoderma lucidum has been used in traditional oriental medicine and has potential antiinflammatory and antioxidant activities. In the present study, we tested the amino-polysacc
Each person has his or her own distinct event-related potential (ERP) signals. Thus, traditional brain-computer interface (BCI) systems require a calibration process in which the subject's data are extracted in order to train machine-learning classifiers. Despite past efforts to eliminate this process, often referred to as “zero-training,” BCI systems' best performance is achievable only with some level of calibration. This tedious process is one of the factors that have limited the use of BCI s
Sulcal pit analysis has been providing novel insights into brain function and development. The purpose of this study was to evaluate the reliability of sulcal pit extraction with respect to the effects of scan session, scanner, and surface extraction tool. Five subjects were scanned 4 times at 3 MRI centers and other 5 subjects were scanned 3 times at 2 MRI centers, including 1 test-retest session. Sulcal pits were extracted on the white matter surfaces reconstructed with both Montreal Neurologi
Considering the crosstalk between the flow and vessel wall, hemodynamic assessment of the neurovascular system may offer a well-integrated solution for both diagnosis and management by adding prognostic significance to the standard CT/MR angiography. 4D flow MRI or time-resolved 3D velocity-encoded phase-contrast MRI has long been promising for the hemodynamic evaluation of the great vessels, but challenged in clinical studies for assessing intracranial vessels with small diameter due to long sc
Currently, research based on the technology and applications of 3D printing is being actively pursued. 3D printing technology, also called additive manufacturing, is widely and increasingly used in the medical field. This study produced custom casts for the treatment of mallet finger using plaster of Paris, which was traditionally used in clinical practice, and 3D printing technology, and evaluated their advantages and disadvantages for patients by conducting a wearability assessment. Mallet fin
IVC filter tilt, external compression on IVC wall, and IVC morphology were significantly different between the filter tip abutment and non-abutment groups. External compression and filter tilt over 9.25° were risk factors for filter tip abutment in multiple logistic regression analysis. By identifying these factors, we may be able to reduce filter tilting by preventing the filter from being deployed in a dangerous area.
In this paper, we introduce a novel automatic method for Corpus Callosum (CC) in midsagittal plane segmentation. The robust segmentation of CC in midsagittal plane is key role for quantitative study of structural features of CC associated with various neurological disorder such as epilepsy, autism, Alzheimer's disease, and so on. Our approach is based on Bayesian inference using sparse representation and multi-atlas voting which both methods are used in various medical imaging, and show outstand
We extracted multi-modal features from automatically defined hippocampal regions of training subjects and found this method to be discriminative and robust for AD and MCI classification. The extraction of features in T1 and FDG-PET images is expected to improve classification performance due to the relationship between brain structure and function.
An integrated study of the valve train including all the key factors of design, dynamic motion, lubrication, and friction was performed. This study consists of a computational simulation with experimental verification. The simulation includes an integrated analysis of kinematic synthesis, vibrational motion, and a tribological study of instantaneous friction and lubrication. Experimental data was used to verify the modeling and to acquire the precise input parameters of the model. The experiment