Jong-Woo Song

Trichothecenes are isoprenoid mycotoxins produced in wheat infected with the filamentous fungus Fusarium graminearum. Some fungal genes for trichothecene biosynthesis (Tri genes) are known to be under control of transcription factors encoded by Tri6 and Tri10. Tri6 and Tri10 deletion mutants were constructed in order to discover additional genes regulated by these factors in planta. Both mutants were greatly reduced in pathogenicity and toxin production and these phenotypes were largely restored

Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation pr

Deep learning has made great strides in the field of unstructured data such as text, images, and audio.However, in the case of tabular data analysis, machine learning algorithms such as ensemble methods are still better than deep learning.To keep up with the performance of machine learning algorithms with good predictive power, several deep learning methods for tabular data have been proposed recently.In this paper, we review the latest deep learning models for tabular data and compare the perfo

This paper studies bias correction methods for Random Forest in regression. Random Forest is a special bagging trees that can be used in regression and classification. It is a popular method because of its high prediction accuracy. However, we find that Random Forest can have significant bias in regression at times. We propose a method to reduce the bias of Random Forest in regression using residual rotation. The real data applications show that our method can reduce the bias of Random Forest si

BACKGROUND: A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. RESULTS: We have

We analyzed Korean professional basketball and baseball players salary under theassumption that it depends on the personal records and contribution to the team in theprevious year. We extensively used data visualization tools to check the relationshipamong the variables, to nd outliers and to do model diagnostics. We used multiplelinear regression and regression tree to t the model and used cross-validation to ndan optimal model. We check the relationship between variables carefully and chose as

This paper predicts gross box office revenue for domestic films using the Korean film data from 2008--2011. We use three regression methods, Linear Regression, Random Forest and Gradient Boosting to predict the gross box office revenue. We only consider domestic films with a revenue size of at least KRW 500 million; relevant explanatory variables are chosen by data visualization and variable selection techniques. The key idea of analyzing this data is to construct the meaningful explanatory vari

The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of package