• 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.

• 한국정보통신학회논문지
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• 제21권10호
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• pp.1898-1908
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• 2017

가축 질병을 정확하게 예측하고 관리하는 것은 지속가능한 가축의 생산을 보장하고 궁극적으로 인간의 건강을 유지하는 데 기여한다. 가축의 건강을 유지하는 것은 인간의 건강에 영향을 미치는 중요한 부분이다. 송아지 질병 예측은 송아지의 생체 데이터를 측정하여 전처리 과정을 거친다. 송아지 정보는 송아지의 출산 이력과 송아지 생체 정보측정, 축사의 환경 정보, 질병 관리를 위한 정보를 사용하며, 온톨로지 개발하여 지식 베이스로 활용하였고, 송아지의 질병에 대한 상호 연관성을 분석 과정하기 위해 베이지안 네크워크를 사용하여 추론하였다. 송아지 질병, 원인, 발생 시기, 증상 등에 대한 송아지 질병 지식을 기반으로 질병을 예측하면 그 결과로 정확한 질병 치료에 대응할 수 있고 다른 가축에게 전염되는 것을 사전에 방지할 수 있을 것이다.

• Korean Circulation Journal
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• 제53권9호
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• pp.581-593
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• 2023

In the catheterization laboratory, the measurement of physiological indexes can help identify functionally significant lesions and has become one of the standard methods to guide treatment decision-making. Plaque vulnerability refers to a coronary plaque susceptible to rupture, enabling risk prediction before coronary events, and it can be detected by defining a certain type of plaque morphology on coronary imaging modalities. Although coronary physiology and plaque vulnerability have been considered different attributes of coronary artery disease, the underlying pathophysiological basis and clinical data indicate a strong correlation between coronary hemodynamic properties and vulnerable plaque. In prediction of coronary events, emerging data have suggested independent and additional implications of a physiology-based approach to a plaque-based approach. This review covers the fundamental interplay between coronary physiology and plaque morphology during disease progression with clinical data supporting this relationship and examines the clinical relevance of physiological indexes in prediction of clinical outcomes and therapeutic decision-making along with plaque vulnerability.

• 한국멀티미디어학회논문지
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• 제16권3호
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• pp.269-280
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• 2013

본 논문에서 망막 질환 요인간의 의존도 분석을 위해 효율적인 분류기를 활용할 수 있는 방안을 제시하였다. 먼저 여러 베이지안 네트워크 중에서 TAN (Tree-Augmented Naive Bayesian Network), GBN(General Bayesian Network)과 Markov Blanket으로 특징축소된 GBN과의 분류성능과 예측정확률을 비교분석하였다. 그리고 처음으로, 높은 성능을 보인 TAN을 망막 질환 임상데이터의 의존도 분석에 적용하였다. 의존도 분석 결과, 망막 질환의 진단과 예후 예측에 활용의 가능성을 보였다.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• 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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권6호
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• pp.303-308
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• 2017

생활 수준의 향상 및 소비자들의 건강에 대한 관심의 증가로 인해 자신의 건강에 대해서 스스로 결정하고자 하는 요구가 점차 증가하고 있다. 이로 인해 개인 맞춤형 의료에 대한 요구가 높아지고 있으며 각종 의료 정보를 기반으로 하는 질병 진단에 대한 연구가 많이 진행되고 있다. 하지만 기존의 연구들은 특정 질환과 관련된 데이터를 이용한 특정 질환 예측을 위한 것으로 진료과목을 예측한 연구는 없었다. 본 논문에서는 국민건강정보데이터를 이용하여 진료과목 예측에 관한 연구를 진행하였다. 실험 결과에서 보여주다시피 일반 건강검진 데이터를 이용하여 진료과목을 예측한 결과 평균 80% 이상의 정확도를 보여 주고 있으며 SVM은 다른 예측 알고리즘들보다 뛰어난 성능을 보여 주었다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• International Journal of Industrial Entomology and Biomaterials
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• 제6권2호
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• pp.163-169
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• 2003

Studies were conducted on the effect of pruning time, host age, conidial dispersal and weather parameters on the incidence and severity of mulberry leaf spot (Myrothecium roridum). The disease severity (%) increased with increase in shoot age irrespective of pruning date. Maximum disease severity was observed in plants pruned during first week of April and minimum disease severity in plants pruned during first week of March. Significant (P ＜ 0.01) influence of date of pruning, shoot age and their interaction was observed on severity of the disease. Apparent infection rate (r) was significantly higher during the plant growth period from day 48 to day 55. Average apparent yale was higher in plants pruned during first week of April and least in plants pruned during first week of July. The disease infection was negatively correlated to distance from the inoculum source. Leaf spot severity (%) was influenced by weather parameters. Multiple regression analysis revealed contribution of various combinations of weather parameters on the disease severity. Linear prediction model $(Y ＝ －81.803＋1.176x_2＋0.765x_3) with significant $R^2$ was developed for prediction of the disease under natural epiphytotic condition.

• The Plant Pathology Journal
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• 제38권4호
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.