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Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions

 Ki-Yeol Kim  ;  Dong Hyuk Ki  ;  Hei-Cheul Jeung  ;  Hyun Cheol Chung  ;  Sun Young Rha 
 BMC BIOINFORMATICS, Vol.9 : 283, 2008 
Journal Title
Issue Date
Algorithms* ; Databases, Genetic* ; Gene Expression Profiling/methods* ; Oligonucleotide Array Sequence Analysis/methods* ; Proteome/metabolism* ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Transduction/physiology*
Prediction Accuracy ; cDNA Microarrays ; Minimal Entropy ; Informative Gene ; Gene Expression Ratio
BACKGROUND: The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. More than that, even when the data sets were generated from the same platform, the data agreement may be affected by the technical variation among the laboratories. In this case, it is necessary to use the combined data set after adjusting the differences between such data sets, for detecting the more reliable information.

RESULTS: The proposed method combines data sets posterior to the discretization of data sets based on the ranks of the gene expression ratios, and the statistical method is applied to the combined data set for predictive gene selection. The efficiency of the proposed method was evaluated using five colon cancer related data sets, which were experimented using cDNA microarrays with different RNA sources, and one experiment utilized oligonucleotide arrays. NCI-60 cell lines data sets were used, which were performed with two different platforms of cDNA microarrays and Affymetrix HU6800 oligonucleotide arrays. The combined data set by the proposed method predicted the test data sets more accurately than the separated data sets did. The biological significant genes were detected from the combined data set, which were missed on the separated data sets.

CONCLUSION: By transforming gene expressions using ranks, the proposed method is not influenced by systematic bias among chips and normalization method. The method may be especially more useful to find predictive genes from data sets which have different scale in gene expressions.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Others (기타) > 1. Journal Papers
Yonsei Authors
Kim, Ki Yeol(김기열) ORCID logo https://orcid.org/0000-0001-5357-1067
Rha, Sun Young(라선영) ORCID logo https://orcid.org/0000-0002-2512-4531
Chung, Hyun Cheol(정현철) ORCID logo https://orcid.org/0000-0002-0920-9471
Jeung, Hei Cheul(정희철) ORCID logo https://orcid.org/0000-0003-0952-3679
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