Integration and cross‐validation of high‐throughput gene expression data: comparing heterogeneous data sets

FEBS Letters

Bersini, Hugues¹  Maenhaut, Carine²  Detours, Vincent²  Dumont, Jacques E.² 

[1] IRIDIA, Free University of Brussels, 50 Av. F. Roosevelt, B-1050 Brussels, Belgium;IRIBHM, Free University of Brussels, Bldg C, Campus Erasme, 808 route de Lennik, B-1070 Brussels, Belgium

关键词: Bioinformatics;    Microarray;    Serial analysis of gene expression;    Data integration;    GO;    Gene Ontology;    MAGE-ML;    Microarray Gene Expression Mark-up Language;    MIAME;    Minimum Information About a Microarray Experiment;    PPI;    protein–protein interaction;    PCR;    polymerase chain reaction;    SAGE;    Serial Analysis of Gene Expression;   

DOI  :  10.1016/S0014-5793(03)00522-2

学科分类：生物化学/生物物理

来源: John Wiley & Sons Ltd.

【 摘 要 】

Data analysis – not data production – is becoming the bottleneck in gene expression research. Data integration is necessary to cope with an ever increasing amount of data, to cross-validate noisy data sets, and to gain broad interdisciplinary views of large biological data sets. New Internet resources may help researchers to combine data sets across different gene expression platforms. However, noise and disparities in experimental protocols strongly limit data integration. A detailed review of four selected studies reveals how some of these limitations may be circumvented and illustrates what can be achieved through data integration.

【 授权许可】

Unknown