Yong-Hwan Choi

A Late-Maturing and Whole Crop Silage Rice Cultivar `Mogwoo`

`Mogwoo`, a new high yield and whole crop silage rice (Oryza sativa L.) cultivar, was developed by the rice breeding team of the National Institute of Crop Science, RDA, Suwon, Korea, from 1999 to 2009, and was released in 2010. It was derived in 1999 from a cross between Dasanbyeo, having a high yield, and Suweon431/IR71190-45-2-1. A promising line, SR25848-C99-1-2-1, selected by the pedigree breeding method, was designated the name of `Suweon 519` in 2007. This cultivar has about 155 days of growth period from seeding to heading, and is tolerance to lodging, with erect pubescent leaves as well as a long and thick culm. This cultivar has the same number of tillers per hill and higher spikelet numbers per panicle compared to Nokyang. `Mogwoo` has longer leaves compared with other Tongil-type varieties. This new variety is resistant to grain shattering, leaf blast, bacterial leaf blight, and small brown planthopper. The biomass yield of `Mogwoo` was 1,956 kg/10a in a regional test over three years. The result shows that `Mogwoo` is adaptable to central and south-east plain areas of Korea.

Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel

Due to its high mortality rate and asymptomatic nature, early detection rates of pancreatic ductal adenocarcinoma (PDAC) remain poor. We measured 1000 biomarker candidates in 134 clinical plasma samples by multiple reaction monitoring-mass spectrometry (MRM-MS). Differentially abundant proteins were assembled into a multimarker panel from a training set (n=684) and validated in independent set (n=318) from five centers. The level of panel proteins was also confirmed by immunoassays. The panel including leucine-rich alpha-2 glycoprotein (LRG1), transthyretin (TTR), and CA19-9 had a sensitivity of 82.5% and a specificity of 92.1%. The triple-marker panel exceeded the diagnostic performance of CA19-9 by more than 10% (AUCCA19-9 = 0.826, AUCpanel= 0.931, P < 0.01) in all PDAC samples and by more than 30% (AUCCA19-9 = 0.520, AUCpanel = 0.830, P < 0.001) in patients with normal range of CA19-9 (<37U/mL). Further, it differentiated PDAC from benign pancreatic disease (AUCCA19-9 = 0.812, AUCpanel = 0.892, P < 0.01) and other cancers (AUCCA19-9 = 0.796, AUCpanel = 0.899, P < 0.001). Overall, the multimarker panel that we have developed and validated in large-scale samples by MRM-MS and immunoassay has clinical applicability in the early detection of PDAC.

Comparative studies for developing protein based cancer prediction model to maximise the ROC-AUC with various variable selection methods

The era of protein data analysis is coming with more accurate quantification experiments such as the multiple reaction monitoring MRM. Protein is easier to obtain than the other genetic variants or gene expression data, which makes it more suitable for early diagnosis of cancer. Each patient has unique patterns of protein data, which makes it imperative for the researcher to select the effective markers to construct a consistent model to predict the patients. This research focuses on finding the most effective variable selection method to be applied in the early diagnosis of the pancreatic cancer. In the process, we compare classical selection methods stepwise selection based on AIC, BIC, machine learning based selection method support vector machine recursive feature selection; SVM-REF, and stepwise selection method using the area under the receiver operating characteristic curve Step-AUC. Based on the simulation and real data analysis, we suggest a Step-AUC method to maximise the prediction performance of the early diagnosis by protein data.

Developing cancer prediction model based on stepwise selection by AUC measure for proteomics data

Since most of the cancer markers that have been reported are obtained directly from cancer tissues, it is difficult to use them for early diagnosis of cancer without surgery. Thus, development of markers that can be detected by blood is crucial for making early diagnosis of cancer easier. One of the most feasible types of markers that can be detected by blood is a protein marker. Here, we focus on building prediction methods using the protein markers for early diagnosis of cancer. To develop a prediction model with high prediction ability, it is critical to choose appropriate markers first. Here, we consider a stepwise selection method using area under the receiver operating characteristic curve (Step-AUC) in order to construct a multi-protein prediction model. We showed that the performance of Step-AUC highly depends on the tuning parameter. We compared our proposed Step-AUC method to stepwise selection using information criteria and support vector machine recursive feature extraction (SVM-RFE). We observed that Step-AUC and stepwise selection using Bayesian information criteria (Step-BIC) perform better than other methods. The importance of each marker can be chosen using a new stepwise selection consistency (SSC) measure. The final models include the markers with high SSC measures. We applied our stepwise procedure to pancreatic cancer data and found two markers of interest.