Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method
Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method
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DOI：
10.1093/bioinformatics/bth473
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发表时间：
2005-01
期刊：
影响因子：
5.8
通讯作者：
Hiro Takahashi;Takeshi Kobayashi;H. Honda
中科院分区：
文献类型：
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作者：
Hiro Takahashi;Takeshi Kobayashi;H. Honda
利用DNA微阵列分析技术建立各种疾病的预后预测因子的动机，希望找到选择性显著的基因来构建预后模型，也有必要在构建模型之前剔除非特异性基因或有错误的基因。结果我们将投影自适应共振理论(部分)应用于DNA微阵列数据的基因筛选。我们用我们的FNN-Sweep建模方法对按部分选择的基因进行建模，以构建癌症类别预测模型。通过与传统的筛选信噪比(S2N)方法或最近收缩质心(NSC)方法的比较来评估模型的性能。FNN扫描结合部分筛选的预测因子对盲数据中的急性白血病和肺癌的分类准确率分别为97.1%和90.0%，而S2N和NSC分别为85.3%和70.0%和88.2%和90.0%。结果表明，该片段在基因筛选中具有较高的优势。该软件可根据作者的要求提供。联系honda@nuBio.nagoya-U.S.ac.jp
MOTIVATION For establishing prognostic predictors of various diseases using DNA microarray analysis technology, it is desired to find selectively significant genes for constructing the prognostic model and it is also necessary to eliminate non-specific genes or genes with error before constructing the model. RESULTS We applied projective adaptive resonance theory (PART) to gene screening for DNA microarray data. Genes selected by PART were subjected to our FNN-SWEEP modeling method for the construction of a cancer class prediction model. The model performance was evaluated through comparison with a conventional screening signal-to-noise (S2N) method or nearest shrunken centroids (NSC) method. The FNN-SWEEP predictor with PART screening could discriminate classes of acute leukemia in blinded data with 97.1% accuracy and classes of lung cancer with 90.0% accuracy, while the predictor with S2N was only 85.3 and 70.0% or the predictor with NSC was 88.2 and 90.0%, respectively. The results have proven that PART was superior for gene screening. AVAILABILITY The software is available upon request from the authors. CONTACT honda@nubio.nagoya-u.ac.jp