• Molecules and Cells
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• v.46 no.1
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• pp.21-32
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• 2023

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7381-7384
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• 2015

This study was conducted to 1206 women who had cervical cancer screening at Chonburi Cancer Hospital. The spilt-sample study aimed to compare the efficacy of abnormal cervical cells detection between liquid-based cytology (LBC) and conventional cytology (CC). The collection of cervical cells was performed by broom and directly smeared on a glass slide for CC then the rest of specimen was prepared for LBC. All slides were evaluated and classified by The Bethesda System. The results of the two cytological tests were compared to the gold standard. The LBC smear significantly decreased inflammatory cell and thick smear on slides. These two techniques were not difference in detection rate of abnormal cytology and had high cytological diagnostic agreement of 95.7%. The histologic diagnosis of cervical tissue was used as the gold standard in 103 cases. Sensitivity, specificity, positive predictive value, negative predictive value, false positive, false negative and accuracy of LBC at ASC-US cut off were 81.4, 75.0, 70.0, 84.9, 25.0, 18.6 and 77.7%, respectively. CC had higher false positive and false negative than LBC. LBC had shown higher sensitivity, specificity, PPV, NPV and accuracy than CC but no statistical significance. In conclusion, LBC method can improve specimen quality, more sensitive, specific and accurate at ASC-US cut off and as effective as CC in detecting cervical epithelial cell abnormalities.

• The Korean Journal of Cytopathology
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• v.10 no.1
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• pp.13-19
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• 1999

The authors analysed 2,653 cases of transthoracic fine needle aspiration cytology of the lung to evaluate the diagnostic accuracy and its limitation. A comparison was made between the original cytologic and the final histologic diagnoses on 1,149 cases from 1,074 patients. A diagnosis of malignancy was established in 38.3% benign in 48.1%, atypical lesion in 2.3%, and inadequate one in 11.9% of the cases. Statistical data on cytologic diagnoses were as follows; specificity 98.9%: sensitivity of procedure, 76.8%: sensitivity of diagnosis, 95.5%: false positive 5 cases: false negative 18 cases: predictive value for malignancy, 98.8%: predictive value for benign lesion, 79.5%: overall diagnostic efficiency, 87.5%: typing accuracy in malignant tumor, 80%.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.703-720
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• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• Clinical and Experimental Pediatrics
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• v.55 no.1
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• pp.11-17
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• 2012

Purpose: Early identification of neonatal sepsis is a global issue because of limitations in diagnostic procedures. The objective of this study was to compare the diagnostic accuracy of neutrophil CD64 and C-reactive protein (CRP) as a single test for the early detection of neonatal sepsis. Methods: A prospective study enrolled newborns with documented sepsis (n=11), clinical sepsis (n=12) and control newborns (n=14). CRP, neutrophil CD64, complete blood counts and blood culture were taken at the time of the suspected sepsis for the documented or clinical group and at the time of venipuncture for laboratory tests in control newborns. Neutrophil CD64 was analyzed by flow cytometry. Results: CD64 was significantly elevated in the groups with documented or clinical sepsis, whereas CRP was not significantly increased compared with controls. For documented sepsis, CD64 and CRP had a sensitivity of 91% and 9%, a specificity of 83% and 83%, a positive predictive value of 83% and 33% and a negative predictive value of 91% and 50%, respectively, with a cutoff value of 3.0 mg/dL for CD64 and 1.0 mg/dL for CRP. The area under the receiver-operating characteristic curves for CD64 index and CRP were 0.955 and 0.527 ($P$ <0.01), respectively. Conclusion: These preliminary data show that diagnostic accuracy of CD64 is superior to CRP when measured at the time of suspected sepsis, which implies that CD64 is a more reliable marker for the early identification of neonatal sepsis as a single determination compared with CRP.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.20-29
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• 2021

In the Industry 4.0 era, artificial intelligence has attracted considerable interest for learning mass data to improve the accuracy of forecasting and classification. On the other hand, the current method of detecting anomalies relies on traditional statistical methods for a limited amount of data, making it difficult to detect accurate anomalies. Therefore, this paper proposes an artificial intelligence-based anomaly detection methodology to improve the prediction accuracy and identify new data patterns. In particular, data were collected and analyzed from the point of view that sensor data collected at vehicle idle could be used to detect abnormalities. To this end, a sensor was designed to determine the appropriate time length of the data entered into the forecast model, compare the results of idling data with the overall driving data utilization, and make optimal predictions through a combination of various sensor data. In addition, the predictive accuracy of artificial intelligence techniques was presented by comparing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) as the predictive methodologies. According to the analysis, using idle data, using 1.5 times of the data for the idling periods, and using CNN over LSTM showed better prediction results.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Korean Journal of Radiology
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• v.23 no.1
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• pp.52-59
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• 2022

Objective: To investigate whether the diagnostic performance of CT angiography (CTA) could be improved by modifying the conventional criterion (anastomosis site abnormality) to diagnose hepatic artery occlusion (HAO) after liver transplantation (LT) in suspected patients with Doppler ultrasound (US) abnormalities. Materials and Methods: One hundred thirty-four adult LT recipients (88 males and 46 females; mean age, 52.7 years) with suspected HAO on Doppler US (40 HAO and 94 non-HAO according to the reference standards) were included. We evaluated 1) abnormalities in the HA anastomosis, categorized as a cutoff, ≥ 50% stenosis at the anastomotic site, or diffuse stenosis at both graft and recipient sides around the anastomosis, and 2) abnormalities in the distal run-off, including invisibility or irregular, faint, and discontinuous enhancement. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the conventional (considering anastomosis site abnormalities alone) and modified CTA criteria (abnormalities in both the anastomosis site and distal run-off) for the diagnosis of HAO were calculated and compared using the McNemar test. Results: By using the conventional criterion to diagnose HAO, the sensitivity, specificity, PPV, NPV, and accuracy were 100% (40/40), 74.5% (70/94), 62.5% (40/64), 100% (70/70), and 82.1% (110/134), respectively. The modified criterion for diagnosing HAO showed significantly increased specificity (93.6%, 88/94) and accuracy (93.3%, 125/134) compared to that with the conventional criterion (p = 0.001 and 0.002, respectively), although the sensitivity (92.5%, 37/40) decreased slightly without statistical significance (p = 0.250). Conclusion: The modified criterion considering abnormalities in both the anastomosis site and distal run-off improved the diagnostic performance of CTA for HAO in suspected patients with Doppler US abnormalities, particularly by increasing the specificity.

• Journal of Biomedical Engineering Research
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• v.39 no.2
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• pp.69-79
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• 2018

The present study proposes an algorithm that can discriminate between normal subjects and paroxysmal atrial fibrillation (PAF) patients, which is conducted using electrocardiogram (ECG) without PAF events. For this, time-domain features and random forest classifier are used. Time-domain features are obtained from Poincare plot, Lorenz plot of ${\delta}RR$ interval, and morphology analysis. Afterward, three features are selected in total through feature selection. PAF patients and normal subjects are classified using random forest. The classification result showed that sensitivity and specificity were 81.82% and 95.24% respectively, the positive predictive value and negative predictive value were 96.43% and 76.92% respectively, and accuracy was 87.04%. The proposed algorithm had an advantage in terms of the computation requirement compared to existing algorithm, so it has suggested applicability in the more efficient prediction of PAF.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.177-190
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• 2006

The aim of this study is to propose a Bayesian model for fitting mortality rate of colon cancer. For the analysis of mortality rate of a disease, factors such as age classes of population and spatial characteristics of the location are very important. The model proposed in this study allows the age class to be a random effect in addition to its conventional role as the covariate of a linear regression, while the spatial factor being a random effect. The model is fitted using Metropolis-Hastings algorithm. Posterior expected predictive deviances, standardized residuals, and residual plots are used for comparison of models. It is found that the proposed model has smaller residuals and better predictive accuracy. Lastly, we described patterns in disease maps for colon cancer.