Integration and cross-validation of high-throughput gene expression data: comparing heterogeneous data sets.
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.
Detours
Vincent
V
IRIBHM, Free University of Brussels, Bldg C, Campus Erasme, 808 route de Lennik, B-1070 Brussels, Belgium. vdetours@ulb.ac.be
Dumont
Jacques E
JE
Bersini
Hugues
H
Maenhaut
Carine
C
D000818:Animals; D019295:Computational Biology; D003628:Database Management Systems; D030541:Databases, Genetic; D015870:Gene Expression / Q000235:genetics*; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results