• Genomics & Informatics
• /
• 제6권3호
• /
• pp.136-141
• /
• 2008

A large number of studies have been performed to identify biomarkers that will allow efficient detection and determination of the precise status of a patient’s disease. The use of microarrays to assess biomarker status is expected to improve prediction accuracies, because a whole-genome approach is used. Despite their potential, however, patient samples can differ with respect to biomarker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different experimental conditions. Because of this difficulty in experimental standardization of microarray data, it is currently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a method that predicts disease status using gene expression data that are transformed by their ranks, a concept that is easily applied to two datasets that are obtained using different experimental platforms. NCI and colon cancer datasets, which were assessed using both Affymetrix and cDNA microarray platforms, were used for method validation. Our results demonstrate that the proposed method is able to achieve good predictive performance for datasets that are obtained under different experimental conditions.

• 한국환경보건학회지
• /
• 제35권1호
• /
• pp.45-52
• /
• 2009

In recent years, pharmaceuticals in the aquatic environment have become a matter of increasing public concern. Environmental risk assessment (ERA), including an exposure assessment, is considered the best scientifically based approach for evaluating the potential effects of pharmaceuticals on ecosystems. Computerized exposure models constitute an important tool in predicting environmental exposures of pharmaceuticals. This paper presents the applicability of an exposure model by comparing measured data of selected pharmaceuticals with predicted environmental concentrations from an exposure model. $PhATE^{TM}$ (Pharmaceutical Assessment and Transport Evaluation) model developed by the Pharmaceutical Research and Manufacturers of America (PhRMA) was adapted to run simulations for the Keum River. A set of 7 pharmaceuticals of high production in Korea was modeled. The PECs generated by the $PhATE^{TM}$ model that were then compared to the measured concentrations. The $PhATE^{TM}$ model predicted concentrations for 7 pharmaceuticals including acetaminophen, acetylsalicylic acid, erythromycin, ibuprofen, lincomycin, mefenamic acid, and naproxen were in good agreement with actual measured concentrations, which demonstrated the utility of $PhATE^{TM}$ as a predictive tool. In conclusion, $PhATE^{TM}$, although it does not intend to accurately represent reality, could be utilized for rapid predictions of the environmental concentrations of pharmaceuticals.

• Bulletin of the Korean Chemical Society
• /
• 제30권1호
• /
• pp.67-76
• /
• 2009

The development of a variety of methods like AM1, PM3, PM5 and DFT now allows the calculation of atomic and molecular properties with high precision as well as the treatment of large molecules with predictive power. In this paper, these methods have been used to calculate a number of quantum chemical descriptors (like Klopman atomic softness in terms of $E_n^{\ddag}\;and\;E_m^{\ddag}$, chemical hardness, global softness, electronegativity, chemical potential, electrophilicity index, heat of formation, total energy etc.) for 75 alkanes to predict their boiling point values. The 3D modeling, geometry optimization and semiempirical & DFT calculations of all the alkanes have been made with the help of CAChe software. The calculated quantum chemical descriptors have been correlated with observed boiling point by using multiple linear regression (MLR) analysis. The predicted values of boiling point are very close to the observed values. The values of correlation coefficient ($r^2$) and cross validation coefficient ($r_{cv}^2$) also indicates the generated QSPR models are valuable and the comparison of all the methods indicate that the DFT method is most reliable while the addition of Klopman atomic softness $E_n^{\ddag}$ in DFT method improves the result and provides best correlation.

• 산업경영시스템학회지
• /
• 제28권3호
• /
• pp.26-35
• /
• 2005

This paper is intended to compare the hazard rate estimations from Bayesian approach and maximum likelihood estimate(MLE) method. Hazard rate frequently involves unknown parameters and it is common that those parameters are estimated from observed data by using MLE method. Such estimated parameters are appropriate as long as there are sufficient data. Due to various reasons, however, we frequently cannot obtain sufficient data so that the result of MLE method may be unreliable. In order to resolve such a problem we need to rely on the judgement about the unknown parameters. We do this by adopting the Bayesian approach. The first one is to use a predictive distribution and the second one is a method called Bayesian estimate. In addition, in the Bayesian approach, the prior distribution has a critical effect on the result of analysis, so we introduce the method using computerized-simulation to elicit an effective prior distribution. For the simplicity, we use exponential and gamma distributions as a likelihood distribution and its natural conjugate prior distribution, respectively. Finally, numerical examples are given to illustrate the potential benefits of the Bayesian approach.

• Journal of Information Processing Systems
• /
• 제8권4호
• /
• pp.621-652
• /
• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Journal of Information Processing Systems
• /
• 제13권4호
• /
• pp.914-927
• /
• 2017

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

• 대한의용생체공학회:의공학회지
• /
• 제40권5호
• /
• pp.189-196
• /
• 2019

We investigated differences in ventricular and hippocampal volumes between CSF tap test (CSFTT) responders and non-responders in idiopathic normal-pressure hydrocephalus (INPH) patients and compared these parameters in INPH patients with that of age- and gender-matched healthy controls. We also evaluated relationships between ventricular and hippocampal volumes and clinical profiles in INPH patients. We enrolled 48 patients with INPH and 29 healthy controls. Ventricular and hippocampal volumes were measured on MRI, including 3-dimensional volumetric images. INPH patients, when compared to healthy controls, had significantly larger ventricular and smaller hippocampal volumes. No difference in ventricular and hippocampal volumes was found between CSFTT responders and non-responders in INPH patients. And hippocampal volumes showed significant negative correlations with Clinical Dementia Rating Scale scores, INPH grading scale cognitive scores, Timed Up and Go Test scores, and Unified Parkinson's Disease Rating Scale motor scores in INPH patients. Volumetric assessment of ventricular and hippocampal regions may have no predictive value in differentiating between CSFTT responders and non-responders in INPH patients. Our findings may help us understand the potential pathophysiology of unique symptoms associated with INPH.

• 산업기술연구
• /
• 제39권1호
• /
• pp.41-45
• /
• 2019

Hydrogen sulfide ($H_2S$) is mainly produced along with methane and hydrocarbons in many gas fields as well as hydrodesulfurization processes of crude oils containing sulfur compounds and the emission of $H_2S$ has a considerable effect on both environmental problem and human health aspects due to formation of, e.g. acid rain and smog. In recent years, ionic liquids (ILs) have been proposed as the most promising solvents for $CO_2$ and hazardous pollutants capture, such as $H_2S$ and sulfur dioxide ($SO_2$). In this work, we demonstrate the use of the predictive COSMO-SAC model for the prediction of Henry's law constant of $H_2S$ in ILs. Furthermore, the method is used to screen for potential IL candidates for $H_2S$ capture from a set of 2,624 ILs formed from 82 cations and 32 anions. The effects of cation on the Henry's law constant of $H_2S$ such as (i) the variation of the alkyl chain length on cation, (ii) the substituent of methyl group ($-CH_3$) for H in C(2) position and (iii) the change of ring structure for cation family are clearly predicted by COSMO-SAC model.

• Journal of Korea Trade
• /
• 제24권1호
• /
• pp.35-58
• /
• 2020

Purpose - This study examines the determinants of foreign direct investment (FDI) location choice for Chinese firms, focusing on the agglomeration effect for firms of the same nationality. Design/methodology - The empirical data are China's inward FDI from the top 19 economies (excluding tax havens and Taiwan) in terms of FDI during 1997-2015 and China's outward FDI from the top 18 economies (excluding tax havens). This study uses a random effects generalized least squares model for panel data analysis. Findings - The results confirm that both host countries' costs and market conditions and the degree of agglomeration affect these countries' attractiveness for FDI inflows. Specifically, agglomeration has a significant effect on China's inward and outward FDI. This study confirms that the agglomeration of firms of the same nationality has predictive power for multinational enterprises' FDI location choices. The host countries' real GDP and trade openness also positively affect FDI inflows. Interestingly, however, China's production cost has a positive effect. Thus, inward FDI aimed at entering the Chinese market is increasing in recent years relative to the previous efficiency-seeking FDI. Inward FDI in China is therefore the market-entry type, whereas outward FDI by Chinese firms is the market-oriented type. Originality/value - These results suggest that the effects of the potential determinants of Chinese outward FDI are similar to those of inward FDI as China's trade liberalization progresses.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• 제30권4호
• /
• pp.145-152
• /
• 2019

Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.