• KOGO NEWS
• /
• v.6 no.1
• /
• pp.29-33
• /
• 2006

Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

• Genomics & Informatics
• /
• v.14 no.4
• /
• pp.160-165
• /
• 2016

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.

• Archives of Pharmacal Research
• /
• v.24 no.1
• /
• pp.1-15
• /
• 2001

Gene therapy has emerged as a new concept of therapeutic strategies to treat diseases which do not respond to the conventional therapies. The principle of gene therapy is to Introduce genetic materials into patient cells to produce therapeutic proteins in these cells. Gene therapy is now at the stage where a number of clinical trials have been carried out to patients with gene-deficiency disease or cancer. Genetic materials for gene therapy are generally composed of gene expression system and gene delivery system. For the clinical application of gene therapy in a way which conventional drugs are used, researches have been focused on the design of gene delivery system which can offer high transfection efficiency with minimal toxicity. Currently, viral delivery systems generally provide higher transfection efficiency compared with non-viral delivery systems while non-viral delivery systems are less toxic, less immunogenic and manufacturable in large scale compared with viral systems. Recently, novel strategies towards the design of new non-viral delivery system, combination of viral and non-viral delivery systems and targeted delivery system have been extensively studied. The continued effort in this area will lead us to develop gene medicine as "gene as a drug" in the near future.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.12
• /
• pp.1189-1195
• /
• 2004

This paper investigates construction of gene (interaction) networks from gene expression time-series data based on evolutionary computation. To illustrate the proposed approach in a comprehensive way, we first assume an artificial gene network and then compare it with the reconstructed network from the gene expression time-series data generated by the artificial network. Next, we employ real gene expression time-series data (Spellman's yeast data) to construct a gene network by applying the proposed approach. From these experiments, we find that the proposed approach can be used as a useful tool for discovering the structure of a gene network as well as the corresponding relations among genes. The constructed gene network can further provide biologists with information to generate/test new hypotheses and ultimately to unravel the gene functions.

• Journal of Yeungnam Medical Science
• /
• v.41 no.1
• /
• pp.13-21
• /
• 2024

Gene therapy involves the introduction of foreign genetic material into host tissue to alter the expression of genetic products. Gene therapy represents an opportunity to alter the course of various diseases. Hence, genetic products utilizing safe and reliable vectors with improved biotechnology will play a critical role in the treatment of various diseases in the future. This review summarizes various important vectors for gene therapy along with modern techniques for potential craniofacial regeneration using gene therapy. This review also explains current molecular approaches for the management and treatment of cancer using gene therapy. The existing literature was searched to find studies related to gene therapy and its role in craniofacial regeneration and cancer treatment. Various databases such as PubMed, Science Direct, Scopus, Web of Science, and Google Scholar were searched for English language articles using the keywords "gene therapy," "gene therapy in present scenario," "gene therapy in cancer," "gene therapy and vector," "gene therapy in diseases," and "gene therapy and molecular strategies."

• Molecules and Cells
• /
• v.42 no.8
• /
• pp.579-588
• /
• 2019

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

• Development and Reproduction
• /
• v.14 no.4
• /
• pp.215-223
• /
• 2010

The construction of transgenic mouse using embryonic stem (ES) cells has been crucial in the functional studies of gene on mouse genome. Gene knockout mice have been powerful for elucidating the function of genes as well as a research model for human diseases. Gene targeting and gene trapping mathods have been the representative technologies for making the knockout mice by using ES cells. Since the gene targeting and the gene trapping methods were independently developed about 20 years ago, it's efficiency and productivity has been improved with a advance of molecular biology. Conventional gene targeting method has been changes to high throughput conditional gene targeting. The combination of the advantage of gene targeting and gene tapping elements allows to extend a spectrum of gene trapping and to improve the efficiency of gene targeting. These advance should be able to produce the mutant with various phenotype to target a certain gene, and in postgenome era they have served as crucial research tools in understanding the functional study of whole genome in mouse.

• Proceedings of the Microbiological Society of Korea Conference
• /
• 2002.10a
• /
• pp.109-115
• /
• 2002

Gene therapy is to treat and cure diseases by an introduction of therapeutic genes in defective cells or tissues of human body. Gene delivery system, gene expression system, and therapeutic gene are three core elements for gene therapy. The efficient delivery of therapeutic genes and appropriate gene expression are the crucial issues for therapeutic outcome of gene delivery. Because it can be used in common for the treatment and cure of various diseases, gene delivery system is the most important core element for a successful gene therapy. Viruses are naturally evolved to transfer their genomes into host cells efficiently. This ability has made vectorologists exploit viruses as attractive vehicles for the delivery of therapeutic genes. Viral vectors based on adenovirus (Ad) and adeno-associated virus (AAV) have been often used for gene delivery in laboratory. Ad and AAV vectors derived from human DNA viruses differ greatly in their life cycle, expression level and duration of transgenes, immunogenicity, and vector preparation. Both vectors can be used as effective tools for gene therapy and more recently in functional genomics. Here, the characteristics of Ad and AAV vectors are discussed.

• Genomics & Informatics
• /
• v.2 no.1
• /
• pp.19-29
• /
• 2004

Gene expression profiles may offer more information than morphology and provide an alternative to morphology- based tumor classification systems. Informative gene selection is finding gene subsets that are able to discriminate between tumor types, and may have clear biological interpretation. Gene selection is a fundamental issue in gene expression based tumor classification. In this report, techniques for selecting informative genes are illustrated and supervised shaving introduced as a gene selection method in the place of a clustering algorithm. The supervised shaving method showed good performance in gene selection and classification, even though it is a clustering algorithm. Almost selected genes are related to leukemia disease. The expression profiles of 3051 genes were analyzed in 27 acute lymphoblastic leukemia and 11 myeloid leukemia samples. Through these examples, the supervised shaving method has been shown to produce biologically significant genes of more than $94\%$ accuracy of classification. In this report, SVM has also been shown to be a practicable method for gene expression-based classification.

• The Plant Pathology Journal
• /
• v.17 no.2
• /
• pp.83-87
• /
• 2001

Disease resistance in plants is often controlled by gene-for-gene mechanism in which avirulence (avr) gene products encoding by pathogens are specifically recognized, either directly or indirectly by plant disease resistance (R) gene products. Recent studies arising from molecular cloning of a number of R genes from various plant species that confer resistance to different pathogens and corresponding avr genes from various pathogens resulted in the accumulation of a wealth of knowledge on mode of action of gene-for-gene interaction. Specially, members of the NBS-LRR class of R genes encoding proteins containing a nucleotide binding site (NBS) and carboxyl-terminal leucine-rich repeats (LRRs) confer resistance to very different types of phytopathogens, such as bacteria, fungi, oomycetes, viruses, nematodes and aphids. This article reviewed the molecular events that occur up-stream of defense response pathway, specially, bacterial avr gene protein recognition mediated by NBS-LRR type R gene product in plant based on current research results of well studied model plants.