Data Mining and Meta-Analysis on DNA Microarray Data

2013 ◽  
pp. 1196-1236
Author(s):  
Triantafyllos Paparountas ◽  
Maria Nefeli Nikolaidou-Katsaridou ◽  
Gabriella Rustici ◽  
Vasilis Aidinis
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Microarray Data ◽  
Meta Analysis ◽  
Biological Significance ◽  
Biological Information ◽  
Dna Microarray Data ◽  
Gene Expression Arrays ◽  
Expression Genetics ◽  
Public Repositories

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

Data Mining and Meta-Analysis on DNA Microarray Data

2012 ◽  
Vol 1 (3) ◽  
pp. 1-39
Author(s):  
Triantafyllos Paparountas ◽  
Maria Nefeli Nikolaidou-Katsaridou ◽  
Gabriella Rustici ◽  
Vasilis Aidinis
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Microarray Data ◽  
Meta Analysis ◽  
Biological Significance ◽  
Biological Information ◽  
Expression Arrays ◽  
Dna Microarray Data ◽  
Gene Expression Arrays ◽  
Expression Genetics

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Data Mining in Gene Expression Analysis

2008 ◽  
pp. 1643-1673
Author(s):  
Jilin Han ◽  
Le Gruenwald ◽  
Tyrrell Conway
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Biological Information ◽  
Future Research ◽  
Gene Expression Data Analysis ◽  
Experimental Conditions ◽  
Single Experiment ◽  
Important Approach ◽  
Future Research Directions ◽  
Gene Expression Levels

The study of gene expression levels under defined experimental conditions is an important approach to understand how a living cell works. High-throughput microarray technology is a very powerful tool for simultaneously studying thousands of genes in a single experiment. This revolutionary technology results in an extensive amount of data, which raises an important question: how to extract meaningful biological information from these data? In this chapter, we survey data mining techniques that have been used for clustering, classification and association rules for gene expression data analysis. In addition, we provide a comprehensive list of currently available commercial and academic data mining software together with their features. Lastly, we suggest future research directions.

Data Mining Methods for Microarray Data Analysis

2011 ◽  
pp. 283-287
Author(s):  
Lei Yu ◽  
Huan Liu
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Future Research ◽  
Gene Expression Microarray ◽  
Expression Microarray ◽  
Class Prediction ◽  
Gene Expression Microarray Data

The advent of gene expression microarray technology enables the simultaneous measurement of expression levels for thousands or tens of thousands of genes in a single experiment (Schena, et al., 1995). Analysis of gene expression microarray data presents unprecedented opportunities and challenges for data mining in areas such as gene clustering (Eisen, et al., 1998; Tamayo, et al., 1999), sample clustering and class discovery (Alon, et al., 1999; Golub, et al., 1999), sample class prediction (Golub, et al., 1999; Wu, et al., 2003), and gene selection (Xing, Jordan, & Karp, 2001; Yu & Liu, 2004). This article introduces the basic concepts of gene expression microarray data and describes relevant data-mining tasks. It briefly reviews the state-of-the-art methods for each data-mining task and identifies emerging challenges and future research directions in microarray data analysis.

Microarray Data Mining

2010 ◽  
pp. 23-47
Author(s):  
Giulia Bruno ◽  
Alessandro Fiori
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Single Experiment ◽  
Classification Feature ◽  
Building Models ◽  
Critical Issues ◽  
Highly Correlated ◽  
Genetic Profiles

Microarray technology is a powerful tool to analyze thousands of gene expression values with a single experiment. Due to the huge amount of data, most of recent studies are focused on the analysis and the extraction of useful and interesting information from microarray data. Examples of applications include detecting genes highly correlated to diseases, selecting genes which show a similar behavior under specific conditions, building models to predict the disease outcome based on genetic profiles, and inferring regulatory networks. This chapter presents a review of four popular data mining techniques (i.e., Classification, Feature Selection, Clustering and Association Rule Mining) applied to microarray data. It describes the main characteristics of microarray data in order to understand the critical issues which are introduced by gene expression values analysis. Each technique is analyzed and examples of pertinent literature are reported. Finally, prospects of data mining research on microarray data are provided.

Data Mining in Gene Expression Analysis

2011 ◽  
pp. 375-418
Author(s):  
Jilin Han ◽  
Le Gruenwald ◽  
Tyrrell Conway
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Biological Information ◽  
Future Research ◽  
Gene Expression Data Analysis ◽  
Experimental Conditions ◽  
Single Experiment ◽  
Important Approach ◽  
Future Research Directions ◽  
Gene Expression Levels

The study of gene expression levels under defined experimental conditions is an important approach to understand how a living cell works. High-throughput microarray technology is a very powerful tool for simultaneously studying thousands of genes in a single experiment. This revolutionary technology results in an extensive amount of data, which raises an important question: how to extract meaningful biological information from these data? In this chapter, we survey data mining techniques that have been used for clustering, classification and association rules for gene expression data analysis. In addition, we provide a comprehensive list of currently available commercial and academic data mining software together with their features. Lastly, we suggest future research directions.

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