• 한국콘텐츠학회논문지
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• 제9권4호
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• pp.114-120
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• 2009

본 연구는 질병과 관련이 있는 단백질들은 질병 네트워크를 형성함에 있어서 매우 중요한 인자로 작용할 가능성이 있다는 아이디어에서 출발한다. 우리는 Online Medelian Inheritance in Man(OMIM)으로부터 아토피관련 43개 단백질 데이터베이스를 확보하고 이 단백질들과 상호작용하는 단백질 네트워크를 구축하였다. 아토피관련 단백질 네트워크를 바탕으로 질병 네트워크를 구축하였다. 질병 네트워크로부터 질병단백체인 CCR5, CCL11, 및 IL4R을 발굴하였는데, 이들 모두는 단백질 네트워크에서 허브 단백질로 작용하는 것들이다. 허브단백질은 세포에서 필수단백질로 작용하는 것으로 알려져 있는데, 본 연구에서는 허브단백질이면서 동시에 질병에서 매우 중요한 역할을 할 것으로 기대되는 질병단백체로 역할하고 있음을 확인하였다. 본 연구에서 소규모 아토피 관련 질병네트워크를 구축하여 분석하였지만, 여기에 제안한 질병네트워크 분석이 복잡한 인간 질병체계의 분자 기작 및 생물학적 진행과정을 이해하는데 실마리를 제공할 것으로 기대한다.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.103-108
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• 2019

The purpose of this study is to identify the factors that may affect the 5-year survival of colon cancer through network model and to use it as a clinical decision supporting system for colorectal cancer patients. This study was conducted using data from 2,540 patients who underwent colorectal cancer surgery from 1996 to 2018. Eleven factors related to survival of colorectal cancer were selected by consulting medical experts and previous studies. Analysis was proceeded from the data sorted out into 1,839 patients excluding missing values and outliers. Logistic regression analysis showed that age, BMI, and heart disease were statistically significant in order to identify factors affecting 5-year survival of colorectal cancer. Additionally, a correlation analysis was carried out age, BMI, heart disease, diabetes, and other diseases were correlated with 5-year survival of colorectal cancer. Sex was related with BMI, lung disease, and liver disease. Age was associated with heart disease, heart disease, hypertension, diabetes, and other diseases, and BMI with hypertension, diabetes, and other diseases. Heart disease was associated with hypertension, diabetes, hypertension, diabetes, and other diseases. In addition, diabetes and kidney disease were associated. In the correlation analysis, the network model was constructed with the Network Correlation Coefficient less than p <0.001 as the weight. The network model showed that factors directly affecting survival were age, BMI levels, heart disease, and indirectly influencing factors were diabetes, high blood pressure, liver disease and other diseases. If the network model is used as an assistant indicator for the treatment of colorectal cancer, it could contribute to increasing the survival rate of patients.

• Genomics & Informatics
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• 제14권1호
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• pp.2-11
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• 2016

The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.

• 한국지능시스템학회논문지
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• 제25권6호
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• pp.529-535
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• 2015

지금까지 생체 네트워크 분석 연구는 정적(static)인 개념으로만 다루어졌다. 그러나 실제 생명현상이 발생하는 세포 내에서는 세포의 상태 및 외부 환경에 따라 일부 단백질과 그 상호작용만이 선택적으로 활성화된다. 따라서 생체 네트워크의 구조가 시간의 흐름에 따라 변화하는 동적(dynamic)인 개념이 적용되어야 하며, 이런 개념은 질병의 진행 추이를 분석하는데 효율적이다. 본 논문에서는 동적인 네트워크 방법을 알츠하이머 질병에 적용하여 질병이 진행되는 단계에 따라 변화하는 단백질 상호작용 네트워크의 구조적, 기능적 특징에 대하여 분석하고자 한다. 우선, 유전자 발현데이터를 기반으로 각 질병의 진행 상태에 따른 부분 네트워크(정상, 초기, 중기, 말기)를 구축하였다. 이를 기반으로, 네트워크의 구조적 특성 분석을 수행하였다. 또한 기능적 특성 분석을 위해 유전자 군집(module)을 탐색하고, 군집별 유전자 기능(Gene Ontology) 분석을 수행했다. 그 결과, 네트워크의 특성들은 각 질병의 단계와 잘 대응되며, 동적 네트워크 분석법이 중요한 생물학적 이벤트를 설명하는데 이용될 수 있음을 보였다. 결론적으로 제안된 연구 방법을 통하여 그동안 알려지지 않았던 질병유발에 관련된 주요 네트워크 변화를 관측할 수 있고, 질병에 관여하는 복잡한 분자 수준의 발생 기작과 진행 과정을 이해하는데 중요한 정보를 획득할 수 있다.

• Bioinformatics and Biosystems
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• 제2권2호
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• pp.71-74
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• 2007

Cell phenotypes are determined by groups of functionally related genes. Microarray profiling of gene expression provides us response of cellular state to its perturbation. Several methods for uncovering a cellular network show reliable network reconstruction. In this study, we present reconstruction of genetic regulatory network of inflammation bowel disease in human peripheral blood mononuclear cell. The microarray based on Affymetrix Gene Chip Human Genome U133 Array Set HG-U133A is processed and applied network reconstruction algorithm, ARACNe. As a result, we will show that inferred network composed of 450 nodes and 2017 edges is roughly scale-free network and hierarchical organization. The major hub, CCNL2 (cyclin A2), in inferred network is shown to be associated with inflammatory function as well as apoptotic function.

• 한국음향학회지
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• 제38권3호
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• pp.308-313
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• 2019

노년기 3대 질환 중 하나인 파킨슨병은 환자의 70 % 이상이 음성 장애를 앓고 있으며 최근 음성 신호를 통한 파킨슨병의 진단 방법들이 고안되고 있다. 본 논문에서는 음성 특징을 이용한 심층 잔류 순환 신경망 기반의 파킨슨병 진단 방식을 제안한다. 제안하는 방식에서는 파킨슨병 진단을 위한 음성 특징을 선택하고 이를 심층 잔류 순환 신경망에 적용하여 파킨슨병 환자를 식별한다. 제안하는 심층 잔류 순환 신경망은 심층 순환 신경망에 잔류 학습 방식을 결합한 알고리즘으로 파킨슨병 진단에서 기존의 식별 알고리즘보다 더 높은 인식률을 보인다.

• 대한한방내과학회지
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• 제44권1호
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• pp.35-52
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• 2023

Objective: Internal injuries and consumptive disease have different causes, yet they can affect each other. The relationship and combination of prescription drugs in the clinical practice of internal injuries and consumptive disease were analyzed for various diseases of "Donguibogam" through network analysis. Methods: The prescriptions used in consumptive disease and internal injury were established by conducting a full survey on the papers extracted from Donguibogam. The R version 4.0.3 (2020-10-10) and the igraph and arules package were used to perform network analysis and association rule relationship mining analysis in the first and second prescription compositions. Results: The herb frequently used for internal injury was Glycyrrhizae Radix, while the herb combination frequently used was Citri Pericarpium-Glycyrrhizae Radix. For centrality, the main factor was generally Glycyrrhizae Radix. In the case of consumptive disease, the herb most frequently used was Angelicae Gigantis Radix, and the combination most frequently used was Rehmanniae Radix Preparata-Angelicae Gigantis Radix. In terms of centrality, it was Angelicae Gigantis Radix. As a result of the network analysis of herbal prescription frequency, each group was divided into three. Conclusion: The interrelationship between internal injury and consumptive disease prescription drugs may reveal the differences and similarities between internal injury and consumptive disease and may serve as a basis for the development of new drugs or materials that can enhance mutual effectiveness in the treatment of internal injury and consumptive diseases.

• 한국멀티미디어학회논문지
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• 제25권11호
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• pp.1653-1671
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• 2022

Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.

• BMB Reports
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• 제50권11호
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• pp.535-536
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• 2017

A novel approach has been used to identify functional interactions relevant to human disease. Using high-throughput human-yeast genetic interaction screens, a first draft of disease interactome was obtained. This was achieved by first searching for candidate human disease genes that confer toxicity in yeast, and second, identifying modulators of toxicity. This study found potentially disease-relevant interactions by analyzing the network of functional interactions and focusing on genes implicated in amyotrophic lateral sclerosis (ALS), for example. In the subsequent proof-of-concept study focused on ALS, similar functional relationships between a specific kinase and ALS-associated genes were observed in mammalian cells and zebrafish, supporting findings in human-yeast genetic interaction screens. Results of combined analyses highlighted MAP2K5 kinase as a potential therapeutic target in ALS.

• ETRI Journal
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• 제39권6호
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• pp.851-858
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• 2017

Parkinson's disease is a growing and chronic movement disorder, and its diagnosis is difficult especially at the initial stages. In this paper, movement characteristics extracted by a computer using multilayer back propagation neural network mapping are converted to the symptoms of this disease. Then, modulation of three classifiers of C4.5, k-nearest neighbors, and support vector machine with majority voting are applied to support experts in diagnosing the disease. The purpose of this study is to choose appropriate characteristics and increase the accuracy of the diagnosis. Experiments were performed to demonstrate the improvement of Parkinson's disease diagnosis using this method.