1.DNA Chip.
Journal of Korean Society of Endocrinology 2000;15(4-5):463-467
No Abstract Available.
DNA*
;
Oligonucleotide Array Sequence Analysis*
2.A Clustering Tool Using Particle Swarm Optimization for DNA Chip Data.
Genomics & Informatics 2011;9(2):89-91
DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.
Cluster Analysis
;
DNA
;
Oligonucleotide Array Sequence Analysis
3.GraPT: Genomic InteRpreter about Predictive Toxicology.
Jung Hoon WOO ; Yu Rang PARK ; Yong JUNG ; Ji Hun KIM ; Ju Han KIM
Genomics & Informatics 2006;4(3):129-132
Toxicogenomics has recently emerged in the field of toxicology and the DNA microarray technique has become common strategy for predictive toxicology which studies molecular mechanism caused by exposure of chemical or environmental stress. Although microarray experiment offers extensive genomic information to the researchers, yet high dimensional characteristic of the data often makes it hard to extract meaningful result. Therefore we developed toxicant enrichment analysis similar to the common enrichment approach. We also developed web-based system graPT to enable considerable prediction of toxic endpoints of experimental chemical.
Oligonucleotide Array Sequence Analysis
;
Toxicogenetics
;
Toxicology*
4.DNA Microarrays for Comparative Genomics: Identification of Conserved and Variable Sequences in Prokaryotic Genomes.
Genomics & Informatics 2004;2(1):53-56
No abstract available.
DNA*
;
Genome*
;
Genomics*
;
Oligonucleotide Array Sequence Analysis*
5.Implementation of a Particle Swarm Optimization-based Classification Algorithm for Analyzing DNA Chip Data.
Genomics & Informatics 2011;9(3):134-135
DNA chips are used for experiments on genes and provide useful information that could be further analyzed. Using the data extracted from the DNA chips to find useful patterns or information has become a very important issue. In this paper, we explain the application developed for classifying DNA chip data using a classification method based on the Particle Swarm Optimization (PSO) algorithm. Considering that DNA chip data is extremely large and has a fuzzy characteristic, an algorithm that imitates the ecosystem such as the PSO algorithm is suitable to be used for analyzing such data. The application enables researchers to customize the PSO algorithm parameters and see detail results of the classification rules.
DNA
;
Ecosystem
;
Oligonucleotide Array Sequence Analysis
6.Advances in microbial transcriptomics techniques.
Yuanyuan GUO ; Yaru SUN ; Heping ZHANG
Chinese Journal of Biotechnology 2022;38(10):3606-3615
With the rapid development of molecular biotechnology, transcriptomics has been widely used in the study of gene expression. In recent years, the techniques for microbial transcriptomics research have also been rapidly developing. At the gene level, the way for obtaining sequence information has been developed from complementary validation of RNA fragment through DNA microarray to direct sequencing of full-length RNA. Spatially, the traditional population transcriptomics technique has been developed into spatial, single cell and epigenetic transcriptomics studies. With the application of transcriptomics techniques in the field of microbial research, the corresponding defects were gradually revealed and constantly improved. In this paper, the traditional and new transcriptomics techniques in the field of microbial research are summarized to provide reference for microbial transcriptomics research.
Transcriptome
;
Oligonucleotide Array Sequence Analysis
;
Sequence Analysis
;
RNA
;
Biotechnology
7.Global Optimization of Clusters in Gene Expression Data of DNA Microarrays by Deterministic Annealing.
Kwon Moo LEE ; Tae Su CHUNG ; Ju Han KIM
Genomics & Informatics 2003;1(1):20-24
The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non-zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.
Cluster Analysis
;
DNA*
;
Gene Expression*
;
Genomics
;
Oligonucleotide Array Sequence Analysis*
8.Profiling of genes in healthy hGF, aging hGF, healthy hPDLF and inflammatory hPDLF by DNA microarray.
Sang Jun YUN ; Byung Ock KIM ; Jeong Hun YUN ; Dong Wan KANG ; Hyun Seon JANG
The Journal of the Korean Academy of Periodontology 2006;36(3):767-782
No abstract available.
Aging*
;
Cell Aging
;
DNA*
;
Oligonucleotide Array Sequence Analysis*
9.Standard-based Integration of Heterogeneous Large-scale DNA Microarray Data for Improving Reusability.
Yong JUNG ; Hwa Jeong SEO ; Yu Rang PARK ; Jihun KIM ; Sang Jay BIEN ; Ju Han KIM
Genomics & Informatics 2011;9(1):19-27
Gene Expression Omnibus (GEO) has kept the largest amount of gene-expression microarray data that have grown exponentially. Microarray data in GEO have been generated in many different formats and often lack standardized annotation and documentation. It is hard to know if preprocessing has been applied to a dataset or not and in what way. Standard-based integration of heterogeneous data formats and metadata is necessary for comprehensive data query, analysis and mining. We attempted to integrate the heterogeneous microarray data in GEO based on Minimum Information About a Microarray Experiment (MIAME) standard. We unified the data fields of GEO Data table and mapped the attributes of GEO metadata into MIAME elements. We also discriminated non-preprocessed raw datasets from others and processed ones by using a two-step classification method. Most of the procedures were developed as semi-automated algorithms with some degree of text mining techniques. We localized 2,967 Platforms, 4,867 Series and 103,590 Samples with covering 279 organisms, integrated them into a standard-based relational schema and developed a comprehensive query interface to extract. Our tool, GEOQuest is available at http://www.snubi.org/software/GEOQuest/
Data Mining
;
DNA
;
Gene Expression
;
Mining
;
Oligonucleotide Array Sequence Analysis