Publications by authors named "Jongho Im"
A small-data-driven model for predicting adsorption properties in polymeric thin films.
Chem Commun (Camb)
September 2022
Artificial intelligence allowing data-driven prediction of physicochemical properties of polymers is rapidly emerging as a powerful tool for advancing material science. Here, we developed a methodology to use polymer adsorption data as predictable data by analyzing causal relationships between polymer properties and experimental results instead of using big polymer data.
View Article and Find Full Text PDFArticle Synopsis
- The text indicates that there is a correction to a published research article.
- The specific article can be identified by its Digital Object Identifier (DOI), which is 10.1371/journal.pone.0261534.
- Such corrections are typically made to address errors or inaccuracies in the original publication.
Article Synopsis
- Data integration involves merging multiple datasets to gain more insights, specifically using diagnostic medical radiation workers tracked in the National Dose Registry (NDR) linked to mortality and cancer data from 1996 to 2011.
- A survey was conducted from 2012-2013 to assess occupational radiation practices, with data integration performed using the MICE algorithm to handle missing data.
- The analysis revealed differences in health behaviors and demographics between actual and integrated data, highlighting variations based on sex and job type, and demonstrating the efficiency of integrated data for research without additional surveys.
Background: The coronavirus disease 2019 (COVID-19) pandemic has prompted a global-scale public health response. Social distancing, along with intensive testing and contact tracing, has been considered an effective vehicle to reduce new infections. In this study, we aimed to estimate the effect of South Korean public health measures on behavioral changes with respect to social distancing without a nationwide lockdown.
View Article and Find Full Text PDFCost-Effective Extreme Case-Control Design Using a Resampling Method.
Evol Bioinform Online
April 2019
Nested case-control sampling design is a popular method in a cohort study whose events are often rare. The controls are randomly selected with or without the matching variable fully observed across all cohort samples to control confounding factors. In this article, we propose a new nested case-control sampling design incorporating both extreme case-control design and a resampling technique.
View Article and Find Full Text PDFCorrelation coefficient estimates are often attenuated for truncated samples in the sense that the estimates are biased towards zero. Motivated by real data collected in South Sudan, we consider correlation coefficient estimation with singly truncated bivariate data. By considering a linear regression model in which a truncated variable is used as an explanatory variable, a consistent estimator for the regression slope can be obtained from the ordinary least squares method.
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