• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.57-62
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• 2005

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

• Archives of Pharmacal Research
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• 제16권4호
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• pp.271-275
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• 1993

In order to prepare a novel human insulin analogue suhbstituted with homoserine at B$^{30}$ / position, (B$^{30}$ /-homoserine) human insulin, a synthetic gene was designed by linking directly a gene for B chain with that for A chain. This gene was constructed by enzymatic joining of 10 different synthetic oligonucleotides, and then inserted at the polylinker region of pUC19 plasmid. To achieve a high level of gene expression, the gene fusion technique region of pUC19 plasmid. To achieve a high level of gene expression, the gene fusion technique was employed using amino terminal regions of lacZ gene up to Clal or hpal, and either of them has been located under tac promoter. The chemical induction of these fused genes by isopropyl-.betha.-D-thiogalactopyranoside (IPTG) gave a satisfactory level of expression in Escherichia coli harboring the ocnstructed plasmids. It was observed that the fused gene product as a single chain insulin precusor was produced more than 30% of total cell protein of E. coli as a form of inclusion body.

• Journal of Veterinary Science
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• 제23권2호
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• pp.24.1-24.10
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• 2022

Background: Small interfering RNA technology has been considered a prospective alternative antiviral treatment using gene silencing against influenza viruses with high mutations rates. On the other hand, there are no reports on its effectiveness against the highly pathogenic avian influenza H5N1 virus isolated from Indonesia. Objectives: The main objective of this study was to improve the siRNA design based on the nucleoprotein gene (siRNA-NP) for the Indonesian H5N1 virus. Methods: The effectiveness of these siRNA-NPs (NP672, NP1433, and NP1469) was analyzed in vitro in Marbin-Darby canine kidney cells. Results: The siRNA-NP672 caused the largest decrease in viral production and gene expression at 24, 48, and 72 h post-infection compared to the other siRNA-NPs. Moreover, three serial passages of the H5N1 virus in the presence of siRNA-NP672 did not induce any mutations within the nucleoprotein gene. Conclusions: These findings suggest that siRNA-NP672 can provide better protection against the Indonesian strain of the H5N1 virus.

• 유통과학연구
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• 제14권2호
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• pp.33-39
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• 2016

Purpose The purpose of this study is to investigate the impact of gender differences in DNA on consumer buying behavior both online and offline and other buying channels to find out effective sales promotion strategies of enterprises. Research design, data, and methodology - This study investigated the relation between chromosome and DNA, DNA and gene, and gene and human behavior of gender. The study shows generic characteristics have influence upon consumers' buying behavior and inclination, and examined the effects of genetic characteristics depending upon the difference of gender DNA upon consumers' buying behavior. Results - Precedent studies on genetics and ethology showed close relations between chromosome and DNA, DNA and gene, and gene and buying behavior of the gene. 'Hunting and protection', one of the genetic characteristics in men's DNA, had great influence upon the consumers' different buying behavior. Conclusion - Gender DNA difference in genetics and ethology disclosed fundamental reasons for the difference in buying behavior and inclination of men and women. It gives implications that marketing strategies of advertising and sales promotion should be made in different ways depending upon men and women.

• 한국작물학회지
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• 제55권4호
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• pp.371-378
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• 2010

For sensitive and accurate gene expression analysis, normalization of gene expression data against housekeeping genes is required. There are conventional housekeeping gene (e.g. ACT) that primarily function as an internal control of transcription. In this study, we performed an in silico analysis of 278 rice gene expression samples (GSM) in order to identify the gene that is most consistently expressed. Based on this analysis, we identified novel candidate housekeeping genes that displayed improved stability among the cross experimental conditions. Furthermore four of the most conventional housekeeping genes were included in our 30 other housekeeping genes among the most stable genes. Therefore, these 30 genes can he used to normalize transcription results in gene expression studies on rice at a broad range of experimental conditions.

• 원예과학기술지
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• 제29권5호
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• pp.461-466
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• 2011

유전 육종 연구를 위해 연구자들은 실험 목적에 따라 다양한 종류의 프라이머를 제작해야 한다. 인터넷 상에서 다양한 공용 프로그램이 이용되고 있으나 많은 경우 사용자 편의성이 낮기 때문에 유전자의 구조를 고려하여 프라이머를 디자인하기 위해서는 시간과 노력이 소요된다. 본 연구에서는 엑손과 인트론 지역을 시각적으로 구별하면서 손쉽게 프라이머를 제작할 수 있는 프로그램인 Pickprimer를 개발하였다. 이 프로그램은 공용 프로그램인 Spidey와 Primer3 프로그램의 소스 코드를 결합한 후 그래픽 인터페이스를 추가하여 사용자가 유전자의 구조를 예측하고 이를 바탕으로 프라이머를 손쉽게 제작할 수 있게 했다. 입력 정보는 공용 데이터베이스에서 내려 받은 서열을 복사-붙임하여 이용할 수 있게 하였으며, 유전자의 구조를 그림으로 표현하고 동시에 엑손과 인트론 서열을 구별할 수 있게 했다. 이 프로그램을 이용하여 배추의 단일 카피 유전자에 대한 24 쌍의 프라이머를 디자인하고 6개 고정 품종을 대상으로 PCR과 전기영동 실험을 수행한 결과 제작한 모든 프라이머 쌍이 명확한 단일 밴드를 성공적으로 증폭시켰다. 이 프로그램은 분자표지의 개발뿐만 아니라 유전자 기능 연구 등 다양한 종류의 유전 육종 실험에 유용하게 이용될 수 있을 것으로 기대된다.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Journal of Interdisciplinary Genomics
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• 제4권2호
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• pp.19-23
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• 2022

Pseudogenes are genomic regions that contain gene-like sequences that have a high similarity to the known genes but are nonfunctional. They are categorized into processed, unprocessed, and unitary pseudogenes. Unprocessed pseudogenes generated by duplications can be problematic in sequencing approaches in molecular diagnostics. We discuss the risk of misdiagnosis when investigating genes with pseudogenes of high homology, and describe a method for identifying these small and annoying differences between parent genes and pseudogenes, including parent gene-specific assay design.

• Toxicological Research
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• 제19권3호
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• pp.247-257
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• 2003

Medicine is undergoing a revolution in the understanding of the mechanisms through which disease processes develop. The advent of genetics and molecular biology to oncology not only is providing surrogate predictors of therapy response and survival which are forming the basis for selection among established treatment options, but is providing targets for new directions in therapy as well. Molecular modification of somatic cells for the purposes of protecting the normal cells from the toxicity of cancer chemotherapy, for the sensitization of the tumor cells to therapy and use of conditionally replicating viral vector have been new directions of cancer treatment which have reached the clinical arena. Advances in molecular pharmacology and vector design summarized in this paper may provide solutions to some of the existing problems in the technology of gene transfer therapy. Continued basic research into the biological basis of human disease, systemic studies of the application of these discoveries to therapy and the improvement of vector for gene delivery all combined may result in advances in this important field of therapy over the next few years.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.