• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.131-138
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• 2017

New drug development is time-consuming and costly. Hence, it is necessary to repurpose old drugs for finding new indication. We suggest the way that repurposing old drug using massive literature data and biological network. We supposed a disease-drug relationship can be available if signal pathways of the relationship include significant genes identified in literature data. This research is composed of three steps-identifying significant gene using co-occurrence in literature; analyzing the shortest path on biological network; and scoring a relationship with comparison between the significant genes and the shortest paths. Based on literatures, we identify significant genes based on the co-occurrence frequency between a gene and disease. With the network that include weight as possibility of interaction between genes, we use shortest paths on the network as signal pathways. We perform comparing genes that identified as significant gene and included on signal pathways, calculating the scores and then identifying the candidate drugs. With this processes, we show the drugs having new possibility of drug repurposing and the use of our method as the new method of drug repurposing.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.1-6
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• 2012

In modern society, the amount of information has been significantly increased according to the development of IT convergence technology. That leads to develop information obtaining and searching technologies from lots of data. However, they don't seem to have been actually effective for users' health care and promotion not only for limitations of Web-based information and but for lack of personalization. Health information support services have been generally developed in a format of inputting data on bio-information acquired into the computer for the existing network-based transmission. In this paper, a life habits disease system that uses the existing medical data and Framingham risk factor(FRS) performs neural network is proposed. Based on the proposed system, it is possible to provide the fundamental data and guideline to doctors for recognizing the life habits disease diagnosis of patients and that represents increases in the welfare of patients.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Korean Journal of Radiology
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• v.24 no.3
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• pp.247-258
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• 2023

Objective: To localize the neuroanatomical substrate of rapid eye movement sleep behavior disorder (RBD) and to investigate the neuroanatomical locational relationship between RBD and α-synucleinopathy neurodegenerative diseases. Materials and Methods: Using a systematic PubMed search, we identified 19 patients with lesions in different brain regions that caused RBD. First, lesion network mapping was applied to confirm whether the lesion locations causing RBD corresponded to a common brain network. Second, the literature-based RBD lesion network map was validated using neuroimaging findings and locations of brain pathologies at post-mortem in patients with idiopathic RBD (iRBD) who were identified by independent systematic literature search using PubMed. Finally, we assessed the locational relationship between the sites of pathological alterations at the preclinical stage in α-synucleinopathy neurodegenerative diseases and the brain network for RBD. Results: The lesion network mapping showed lesions causing RBD to be localized to a common brain network defined by connectivity to the pons (including the locus coeruleus, dorsal raphe nucleus, central superior nucleus, and ventrolateral periaqueductal gray), regardless of the lesion location. The positive regions in the pons were replicated by the neuroimaging findings in an independent group of patients with iRBD and it coincided with the reported pathological alterations at post-mortem in patients with iRBD. Furthermore, all brain pathological sites at preclinical stages (Braak stages 1-2) in Parkinson's disease (PD) and at brainstem Lewy body disease in dementia with Lewy bodies (DLB) were involved in the brain network identified for RBD. Conclusion: The brain network defined by connectivity to positive pons regions might be the regulatory network loop inducing RBD in humans. In addition, our results suggested that the underlying cause of high phenoconversion rate from iRBD to neurodegenerative α-synucleinopathy might be pathological changes in the preclinical stage of α-synucleinopathy located at the regulatory network loop of RBD.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.150-156
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• 2023

For a doctor, diagnosing a patient's heart disease is not easy. It takes the ability and experience with high flying hours to be able to accurately diagnose the type of patient's heart disease based on the existing factors in the patient. Several studies have been carried out to develop tools to identify types of heart disease in patients. However, most only focus on the results of patient answers and lab results, the rest use only echocardiography data or electrocardiogram results. This research was conducted to test how accurate the results of the classification of heart disease by using two medical data, namely echocardiography and electrocardiogram. Three treatments were applied to the two medical data and analyzed using the decision tree approach. The first treatment was to build a classification model for types of heart disease based on echocardiography and electrocardiogram data, the second treatment only used echocardiography data and the third treatment only used electrocardiogram data. The results showed that the classification of types of heart disease in the first treatment had a higher level of accuracy than the second and third treatments. The accuracy level for the first, second and third treatment were 78.95%, 73.69% and 50%, respectively. This shows that in order to diagnose the type of patient's heart disease, it is advisable to look at the records of both the patient's medical data (echocardiography and electrocardiogram) to get an accurate level of diagnosis results that can be accounted for.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.35 no.4
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• pp.75-94
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• 2022

Objectives : To identify the active ingredient of Poncirus Trifoliata Immaturus and to explore the mechanism expected to potentially act on dermatitis accompanied by pruritus. Methods : We conducted the network pharmacological analysis. We selected effective ingredients among the active compounds of Poinciri Fructus Immaturus. We found the target protein of the selected active ingredient, disease(dermatitis accompanied by pruritus) and fexofenadine. Then we established the network between the proteins which Poinciri Fructus Immaturus and fexofenadine intersected with disease respectively, and the coregene was also extracted. After that, the active pathways in the human body involving the groups and coregenes were searched. Results : Total of 7 active ingredients were selected, and 202 target proteins were collected. There were 756 proteins related to inflammatory skin disease accompanied by pruritus, and 75 proteins were related to fexofenadine. 42 proteins crossed by Poinciri Fructus Immaturus with a disease, and 31 proteins crossed by fexofenadine with a disease. 12 proteins were found as a coregene from the proteins that cross Poinciri Fructus Immaturus and disease. Coregenes are involved in 'Nitric-oxide synthase regulator activity', 'Epidermal growth factor receptor signaling pathway'. 2 groups that extracted are invloved in 'Fc receptor signaling pathway', 'Central carbon metabolism in cancer', 'Phosphatidylinositol 3-kinase complex, class IB', and 'omega-hydroxylase P450 pathway'. Conclusion : It is expected that Poinciri Fructus Immaturus will be able to show direct or indirect anti-pruritus and anti-inflammatory effects on skin inflammation accompanied by pruritus in the future. And it is also expected to have a synergy effect with fexofenadine on skin disease.

• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.131-135
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• 2018

The heart disease taking the second place of the cause of the death of modern people is a terrible disease that makes sudden death without noticing. To judge the aortic valve disease of heart diseases a name of disease was diagnosed using psychological data provided from physioNet. Aortic valve is a valve of the area that blood is spilled from left ventricle to aorta. Aortic stenosis of heart troubles is a disease when the valve does not open appropriately in contracting the left ventricle to aorta due to narrowed aortic valve. In this paper, 3126 samples of cardiac sound data were used as an experiment data composed of 180 characteristics including normal people and aortic valve stenosis patients. To diagnose normal and aortic valve stenosis patients, NEWFM was utilized. By using an average method of weight as an feature selection method of NEWFM, the result shows 91.0871% accuracy.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.627-637
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• 2020

In this paper, the 1.24 million elderly patient medical data (HIRA-APS-2014-0053) provided by the Health Insurance Review and Assessment Service and weather data are analyzed with generalized estimating equation (GEE) model and long short term memory (LSTM) based recurrent neural network (RNN) model to predict the number of disease occurrence. To this end, we estimate the patient's residence as the area of the served medical institution, and the local weather data and medical data were merged. The status of disease occurrence is divided into three categories(occurrence of disease of interest, occurrence of other disease, no occurrence) during a week. The probabilities of categories are estimated by the GEE model and the RNN model. The number of cases of categories are predicted by adding the probabilities of categories. The comparison result shows that predictions of RNN model are more accurate than that of GEE model.

• Genomics & Informatics
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• v.21 no.1
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• pp.4.1-4.18
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• 2023

Coronavirus disease 2019 (COVID-19) is an inflammatory and infectious disease caused by severe acute respiratory syndrome coronavirus 2 virus with a complex pathophysiology. While COVID-19 vaccines and boosters are available, treatment of the disease is primarily supportive and symptomatic. Several research have suggested the potential of herbal medicines as an adjunctive treatment for the disease. A popular herbal medicine approved in the Philippines for the treatment of acute respiratory disease is Vitex negundo L. In fact, the Department of Science and Technology of the Philippines has funded a clinical trial to establish its potential as an adjunctive treatment for COVID-19. Here, we utilized network pharmacology and molecular docking in determining pivotal targets of Vitex negundo compounds against COVID-19. The results showed that significant targets of Vitex negundo compounds in COVID-19 are CSB, SERPINE1, and PLG which code for cathepsin B, plasminogen activator inhibitor-1, and plasminogen, respectively. Molecular docking revealed that α-terpinyl acetate and geranyl acetate have good binding affinity in cathepsin B; 6,7,4-trimethoxyflavanone, 5,6,7,8,3',4',5'-heptamethoxyflavone, artemetin, demethylnobiletin, gardenin A, geranyl acetate in plasminogen; and 7,8,4-trimethoxyflavanone in plasminogen activator inhibitor-1. While the results are promising, these are bound to the limitations of computational methods and further experimentation are needed to completely establish the molecular mechanisms of Vitex negundo against COVID-19.