• Genomics & Informatics
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• 제11권4호
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.211-221
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• 2024

Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches.

• 한국유전체학회:학술대회논문집
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• 한국유전체학회 2005년도 The 14th Korea Genome Conference
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• pp.46-46
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• 2005

• BMB Reports
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• 제54권6호
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• pp.323-328
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• 2021

Periodontal diseases have been reported to have a multidirectional association with metabolic disorders. We sought to investigate the correlation between periodontitis and diabetes or fatty liver disease using HFD-fed obese mice inoculated with P. gingivalis. Body weight, alveolar bone loss, serological biochemistry, and glucose level were determined to evaluate the pathophysiology of periodontitis and diabetes. For the evaluation of fatty liver disease, hepatic nonalcoholic steatohepatitis (NASH) was assessed by scoring steatosis, inflammation, hepatocyte ballooning and the crucial signaling pathways involved in liver metabolism were analyzed. The C-reactive protein (CRP) level and NASH score in P. gingivalis-infected obese mice were significantly elevated. Particularly, the extensive lobular inflammation was observed in the liver of obese mice infected with P. gingivalis. Moreover, the expression of metabolic regulatory factors, including peroxisome proliferator-activated receptor γ (Pparγ) and the fatty acid transporter Cd36, was up-regulated in the liver of P. gingivalis-infected obese mice. However, inoculation of P. gingivalis had no significant influence on glucose homeostasis, insulin resistance, and hepatic mTOR/AMPK signaling. In conclusion, our results indicate that P. gingivalis can induce the progression of fatty liver disease in HFD-fed mice through the upregulation of CD36-PPARγ axis.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• 한국컴퓨터정보학회논문지
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• 제21권3호
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• pp.65-71
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• 2016

To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer's disease and 0.71 on Breast cancer.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• 한국임상수의학회지
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• 제33권5호
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• pp.278-285
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• 2016

Epidemiological studies have shown the association between transportation of live animals and the potential transmission of infectious disease between premises. This finding was also observed in the 2014-2015 foot-and-mouth disease (FMD) outbreak in Korea. Furthermore, slaughterhouses played a key role in the global spread of the FMD virus during the epidemic. In this context, in-depth knowledge of the structure of direct and indirect contact between slaughterhouses is paramount for understanding the dynamics of FMD transmission. But the social network structure of vehicle movements to slaughterhouses in Korea remains unclear. Hence, the aim of this study was to configure a social network topology of vehicle movements between slaughterhouses for a better understanding of how they are potentially connected, and to explore whether FMD outbreaks can be explained by the network properties constructed in the study. We created five monthly directed networks based on the frequency and chronology of on- and off-slaughterhouse vehicle movements. For the monthly network, a node represented a slaughterhouse, and an edge (or link) denoted vehicle movement between two slaughterhouses. Movement data were retrieved from the national Korean Animal Health Integrated System (KAHIS) database, which tracks the routes of individual vehicle movements using a global positioning system (GPS). Electronic registration of livestock movements has been a mandatory requirement since 2013 to ensure traceability of such movements. For each of the five studied networks, the network structures were characterized by small-world properties, with a short mean distance, a high clustering coefficient, and a short diameter. In addition, a strongly connected component was observed in each of the created networks, and this giant component included 94.4% to 100% of all network nodes. The characteristic hub-and-spoke type of structure was not identified. Such a structural vulnerability in the network suggests that once an infectious disease (such as FMD) is introduced in a random slaughterhouse within the cohesive component, it can spread to every other slaughterhouse in the component. From an epidemiological perspective, for disease management, empirically derived small-world networks could inform decision-makers on the higher potential for a large FMD epidemic within the livestock industry, and could provide insights into the rapid-transmission dynamics of the disease across long distances, despite a standstill of animal movements during the epidemic, given a single incursion of infection in any slaughterhouse in the country.

• 대한지역사회영양학회지
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• 제4권3호
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• pp.421-430
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• 1999

A neural network system was applied in order to analyze the nutritional and other factors influencing chronic diseases. Five different nutrition evaluation methods including SD Score, %RDA, NAR INQ and %RDA-SD Score were utilized to facilitate nutrient data for the system. Observing top three chronic disease prediction ratio, WHR using SD Score was the most frequently quoted factor revealing the highest predication rate as 62.0%. Other high prediction rates using other data processing methods are as follows. Prediction rate with %RDA, NAR, INQ and %RDA-SD Score were 58.5%(diabetes), 53.5%(hyperlipidemia), 51.6%(diabetes), and 58.0%(diabetes)respectively. Higher prediction rate was observed using either NAR or INQ for obesity as 51.7% and 50.9% compared to the previous result using SD Score. After reviewing appearance rate for all chronic disease and for various data processing method used, it was found that iron and vitamin C were the most frequently cited factors resulting in high prediction rate.

• 식품산업과 영양
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• 제7권1호
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• pp.50-54
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• 2002