• International Journal of Computer Science & Network Security
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• v.24 no.11
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• pp.163-169
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• 2024

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder with complex genetic architecture. This disease is the focus point of many bioinformatics kinds of research, where the key goal of these researches is to classify the genes involved in the processes of Alzheimer's and to explore the function of these risk genes in the progress of the disease. For this purpose, we here seek out the best model to detect the genes related to AD with the usage of several feature selection methods. In this study, we compared the efficiency of the feature selection methods with SVM classifier including mRMR, CFS, Chi-Square Test, F-score, and GA was compared. The accuracy of SVM classifier has been calculated with validation methods such as 10-fold cross-validation. We applied these methods to the public AD gene expression dataset consist of 696 samples and 200 gene. The results show that mRMR and F-score obtain high accuracy around 84% with number of genes between 40 to 80.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.602-604
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• 2017

As human life span has increased, people have wanted to live healthier desires. Especially Korea has rapidly entered an aging society, leading to the burden of medical expenses to the increase of disease accompanying aging. To alleviate the burden of medical expenses, prediction and prevention are important rather than treatment of diseases. It is possible to predict and prevent diseases by measuring individual genetic information. In order to utilize individual's genetic information Korea's genetic information is grasped through SNP (800 thousand) and GWAS optimized for the discovery of genetic factors of phenotype and disease of Koreans, The genetic information of each individual is analyzed in the genetic (constitutional) characteristics of the individual. In this thesis we develop a classification index so that we can classify populations of specific chronic diseases (obesity, diabetes or cardiovascular system). Try to develop health care services to manage custom diet and exercise associated with chronic illness.

• Journal of Genetic Medicine
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• v.14 no.2
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• pp.49-55
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• 2017

Pre-eclampsia is known to cause considerable maternal morbidity and mortality. Thus, many studies have examined the etiopathogenesis of pre-eclampsia. While many pathophysiological factors related to pre-eclampsia have been identified, the precise etiopathogenesis of pre-eclampsia remains unclear. Numerous studies have identified factors for the early prediction for pre-eclampsia to lead to preparation and closer observation on pre-eclampsia when it occurs. This article reviews on current studies of biomarkers and genetic factors related to pre-eclampsia, which may be important for developing strategies for early prediction of pre-eclampsia.

• Korean Journal of Biological Psychiatry
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• v.12 no.1
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• pp.3-12
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• 2005

Most suicides(about 90%) occur in the context of psychiatric disorders. Prediction of suicide risk in patients with mental illness is very important in preventing suicide attempts. However, current approaches to predict suicidality are based on clinical history and have low specificity and biological markers are not yet included. Many studies have explored the association between different biological parameters and suicidality. Studies of cerebro-spinal fluid(CSF) demonstrated that 5-HIAA and HVA levels were lower in patients with a history of suicide. Platelet serotonin transporter and the 5-HT2 serotonin receptor have also been studied in relation to violence and suicide. Depressive patients with greater suicidal tendency had significantly lower cholesterol concentrations but some researchers failed to find the correlation. DST non-supression is reported to predict suicidality in major depression. Several studies demonstrated a relationship between intron 7 polymorphism of tryptophan hydroxylase and suicidal behavior. Since suicide is not occurred in a single disease, the systematic and comprehensive study in large samples with various diagnoses is necessary to find the biological and genetic predictors of suicidal behavior.

• The Magazine of the IEIE
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• v.31 no.1
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• pp.58-72
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• 2004

Smart sensors and biochip technologies have received a great deal of attention in recent years not only because of the enormous potential markets in the healthcare expenditures but more importantly because of its great impact on the quality of human life in the future. Collaborative research among BT (Bio Technologies), IT (Information Technologies) and NT (Nano Technologies) will bring us a new paradigm of the healthcare services. Examples include disease prediction based on the genetic tests, personal medicines, point-of-care analysis, rapid and sensitive infectious disease diagnostics, environmental monitoring for chemical or biological warfares, intelligent drug delivery systems etc. In this report, recent accomplishment in the research area on biosensors, DNA chips, Protein Chips and Lab-on-a-chips are reviewed.

• Journal of Genetic Medicine
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• v.6 no.2
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• pp.121-130
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• 2009

Medical Research Center, Seoul National University College of Medicine, Seoul, Korea To analyze a wide variety of polymorphisms in patients with endometriosis is important since this disease has a strong genetic component. Until now, more than 30 Korean studies have been performed in order to elucidate the possible role of specific polymorphisms in the susceptibility to endometriosis. The most meaningful polymorphisms in Korean patients with endometriosis came from studies investigating GSTM1, AhRR, ER-alpha, VEGF, AHSG, and TNF-alpha. However, following studies should be made to confirm the consistency of the data to have some implications in the prediction of endometriosis. In this review, we also present the future direction of the association studies in complex trait disease such as endometriosis.

• Genomics & Informatics
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• v.14 no.4
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.595-598
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• 2013

Background: Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving. Materials and Methods: Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm. Results: Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer. Conclusions: Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1067-1074
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• 2014

In Morocco, breast cancer is the most prevalent cancer in women and a major public health problem. Several Moroccan studies have focused on studying this disease, but more are needed, especially at the genetic and molecular levels. It is therefore interesting to establish the genetic and molecular profile of Moroccan patients with breast cancer. In this paper, we will highlight some pertinent hypotheses that may enhance breast cancer care in Moroccan patients. This review will give a precise description of breast cancer in Morocco and propose some new markers for detection and prediction of breast cancer prognosis.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.13 no.2
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• pp.126-130
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• 2013

Galactosemia is a metabolic disorder inherited by the recessive autosome, and appears by the deficiency of one enzyme out of GALT (Galactose-1-Phosphate Uridyltransferase), GALK (galactokinase), and GALE (epimerase) enzymes, among which the GALT deficiency disease is denominated as classical galactosemia and known to have symptoms such as severe nausea, jaundice, hepatomegaly, sucking difficulty and so on. We report the case of a 16-day-old female baby with the new p.A101D mutation together with p.N413d in the GALT gene analysis found in the neonatal screening test and diagnosed to have galactosemia by the GALT deficiency through the enzyme analysis. For the prognosis prediction, the treatment, the genetic counseling and the prenatal diagnosis of the patients, more detailed genetic diagnosis is required by performing GALT gene analysis, and it is deemed to be necessary to analyze the correlation between the phenotype and the genotype of the domestic galactosemia patients.