microarray data

The Boolean network model allows genetic data to be integrated into the logical network of connections deduced from DNA microarray data.

To correlate microarray data with the promoter site consensus sequence for a specific transcription factor.

High throughput methodologies have increased by several orders of magnitude the amount of experimental microarray data available.

The simplicity of this concept sometimes masks the complexity of capturing and processing microarray data.

RESULTS: To this end we have developed three key ingredients towards standardizing the storage and exchange of microarray data.

Given a microarray data set, articulate all the sources of error in measurement and find them in the particular data set.

We developed and tested several algorithms for inference of the model parameters for the microarray data.

These steps are described here and placed in the context of commercial and public tools available for the analysis of microarray data.

The analysis of microarray data requires biologists to collaborate with bioinformaticians or learn the basics of statistics and programming.

The microarray data is linked with the sequence data, for maximum knowledge extraction.

Measurements and Main Results: Microarray data analysis demonstrated upregulation of CD48 in the lungs of OVA-challenged mice.

This chapter outlines a typical workflow for microarray data analysis.

Many classification algorithms in machine learning have been utilized to analyze microarray data, such as gene signature selection.

It presents numerous methods for analyzing microarray data.

Many software tools for microarray data analysis are available.

Because of the increasing popularity and the large volume of data produced, tools for facile microarray data analysis are essential.

Clustering is the most useful and widely used method of microarray data analysis.

Several classification methods based on DNA gene expression microarray data are introduced in this paper.

We present a web server for Diagnosis and Normalization of MicroArray Data (DNMAD).

The tissue-specific and salt responsive expression profiling for SRWD genes was investigated based on microarray data.

Here, LecRK4. 1 knock-out lines of Arabidopsis thaliana were picked up from microarray data after beta-butyric amino acid (BABA) treatment.

Classification methods used for microarray data analysis

Mining gene expression microarray data of nasopharyngeal carcinoma by literature profiling

Strategy of Dimension Reduction Driven by Differential Expression Analysis for Microarray Data

Partial Least Squares Dimension Reduction for Discriminate Analysis of Microarray Data

Gene selection and classification for microarray data of colon and normal tissues using genetic algorithms

Mining Synchronous and Asynchronous Co-Regulated Gene Clusters from Time Series Microarray Data

Application of nonlinear data analysis methods in cancer diagnosis with microarray data

Constructing Association Networks from Development Cerebella Microarray Data Based on Association Rule Mining

The Comparison of Different Normalization Methods in Microarray Data

Supervised Clustering in Microarray Data Discovery

Multiple Comparison Procedures for Microarray Data

Application of decision tree classifier to analysis of gene microarray data

Advanced Analysis Of Gene Expression Microarray Data

Microarray data classification based on artificial neural network ensembles

Some Thoughts on Cluster Analysis of Microarray Data at COLD Syndrome

DNA Microarray Data Analysis Based on Optimal Orthogonal Centroid Feature Selection

Screen of significant genes with adjustable FPR based on microarray data