Gene expression databases and data mining.

Pascale Anderle; Manuel Duval; Sorin Draghici; Alexander Kuklin; Timothy G Littlejohn; Juan F Medrano; David Vilanova; Matthew Alan Roberts

PubMed · 12664683

Gene expression databases and data mining.

Pascale Anderle·Manuel Duval·Sorin Draghici·Alexander Kuklin·Timothy G Littlejohn·Juan F Medrano·David Vilanova·Matthew Alan Roberts

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Abstract

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

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Breast Neoplasms Database Management Systems Databases, Genetic Documentation Female Gene Expression Profiling Gene Expression Regulation Gene Expression Regulation, Neoplastic Humans Information Storage and Retrieval Oligonucleotide Array Sequence Analysis Sequence Analysis, DNA Software Journal Article Review

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Pascale Anderle, Manuel Duval, Sorin Draghici, Alexander Kuklin, Timothy G Littlejohn, Juan F Medrano, David Vilanova, Matthew Alan Roberts. 2003. Gene expression databases and data mining.. https://pubmed.ncbi.nlm.nih.gov/12664683/

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