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イワモト タカユキ
Takayuki Iwamoto
岩本 高行 所属 川崎医科大学 医学部 臨床医学 乳腺甲状腺外科学 職種 講師 |
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| 論文種別 | 原著 |
| 言語種別 | 英語 |
| 査読の有無 | 査読あり |
| 表題 | Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems. |
| 掲載誌名 | 正式名：BMC bioinformatics 略 称：BMC Bioinformatics ISSNコード：14712105/14712105 |
| 掲載区分 | 国外 |
| 巻・号・頁 | 12,pp.463 |
| 著者・共著者 | Kenneth R Hess, Caimiao Wei, Yuan Qi, Takayuki Iwamoto, W Fraser Symmans, Lajos Pusztai |
| 発行年月 | 2011/12 |
| 概要 | BACKGROUND:Our goal was to examine how various aspects of a gene signature influence the success of developing multi-gene prediction models. We inserted gene signatures into three real data sets by altering the expression level of existing probe sets. We varied the number of probe sets perturbed (signature size), the fold increase of mean probe set expression in perturbed compared to unperturbed data (signature strength) and the number of samples perturbed. Prediction models were trained to identify which cases had been perturbed. Performance was estimated using Monte-Carlo cross validation.RESULTS:Signature strength had the greatest influence on predictor performance. It was possible to develop almost perfect predictors with as few as 10 features if the fold difference in mean expression values were > 2 even when the spiked samples represented 10% of all samples. We also assessed the gene signature set size and strength for 9 real clinical prediction problems in six different breast cancer data sets.CONCLUSIONS:We found sufficiently large and strong predictive signatures only for distinguishing ER-positive from ER-negative cancers, there were no strong signatures for more subtle prediction problems. Current statistical methods efficiently identify highly informative features in gene expression data if such features exist and accurate models can be built with as few as 10 highly informative features. Features can be considered highly informative if at least 2-fold expression difference exists between comparison groups but such features do not appear to be common for many clinically relevant prediction problems in human data sets. |
| DOI | 10.1186/1471-2105-12-463 |
| PMID | 22132775 |