Sungkyunkwan University · Medicine
Professor Kyunga Kim's research lab specializes in statistical genetics and bioinformatics, focusing on the identification and prediction of genetic factors underlying complex human diseases. The lab develops advanced computational and statistical methods—particularly in genome-wide association studies (GWAS) and multifactor dimensionality reduction (MDR)—to detect gene-gene interactions and improve the prediction of disease outcomes such as dementia conversion in mild cognitive impairment. A key emphasis is on enhancing evaluation metrics for genetic classifiers by incorporating effect size and ordinal associations, thereby increasing statistical power, especially in high-dimensional genomic data. The lab also applies machine learning techniques, such as the IML (Interpretable Machine Learning) framework, to generate clinically actionable, individualized predictions for patient prognosis.
Figures are computed from collected data and may differ slightly.
Unraveling the genetic background of common complex traits is a major goal in modern genetics. In recent years, genome-wide association (GWA) studies have been conducted with large-scale data sets of genetic variants. Most of those studies have relied on single-marker approaches that identify single genetic factors individually and can be limited in considering fully the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more po
In this article, we introduce two types of new evaluation measures. First, we develop weighted BA (wBA) that utilizes the quantitative information on the effect size of each multi-locus genotype on a trait. Second, we employ ordinal association measures to assess the performance of MDR classifiers. Simulation studies were conducted to compare the proposed measures with BA, a current measure. Our results showed that the wBA and tau(b) improved the power of MDR in detecting gene-gene interactions.
We were able to propose a predictive algorithm for each aMCI individual's conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.
In this cohort study of 10 years of clinical data, survival outcomes were improved across all stages, with larger increases in patients with stage III to IV disease. The incidence of never-smokers and the use of molecular testing increased.
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