No Abstract Available.
DNA* ; Oligonucleotide Array Sequence Analysis*

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

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*

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

No abstract available.
DNA* ; Genome* ; Genomics* ; Oligonucleotide Array Sequence Analysis*

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

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*

No abstract available.
Aging* ; Cell Aging ; DNA* ; Oligonucleotide Array Sequence Analysis*

DNA microarray is currently the most prominent tool for investigating large-scale gene expression data. Different algorithms for measuring gene expression levels from scanned images of microarray experiments may significantly impact the following steps of functional genomic analyses. Affymetrix(R) recently introduced high-density microarrays and new statistical algorithms in Microarray Suit (MAS) version 5.0(R). Very high correlations (0.92 - 0.97) between the new algorithms and the old algorithms (MAS 4.0) across several species and conditions were reported. We found that the column-wise array correlations had a tendency to be much higher than the row-wise gene correlations, which may be much more meaningful in the following higher-order data analyses including clustering and pattern analyses. In this paper, not only the detailed comparison of the two sets of algorithms is illustrated, but the impact of the introducing new algorithms on the further clustering analysis of microarray data and of possible pitfalls in mixing the old and the new algorithms were also described.
DNA* ; Gene Expression ; Oligonucleotide Array Sequence Analysis* ; Statistics as Topic

No abstract available.
Humans* ; Keratosis, Seborrheic* ; Oligonucleotide Array Sequence Analysis ; Polymerase Chain Reaction