iPcc: a novel feature extraction method for accurate disease class discovery and prediction.
iPcc: a novel feature extraction method for accurate disease class discovery and prediction.
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iPcc：一种新的特征提取方法，用于准确的疾病类别发现和预测
DOI：
10.1093/nar/gkt343
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发表时间：
2013-08
影响因子：
14.9
通讯作者：
Jin Q
中科院分区：
文献类型：
--
作者：
Ren X;Wang Y;Zhang XS;Jin Q
基因表达谱已逐渐成为疾病诊断和分类的常规程序。在过去的十年中，许多计算方法被提出，导致在各个层面上的巨大改进，包括特征选择和算法的分类和聚类。在这项研究中，我们提出了iPcc，一种新的方法，从特征提取的角度，进一步推动基因表达谱技术从实验室到床边。我们定义的“相关性特征空间”的基因表达谱的基础上，通过迭代就业的皮尔逊相关系数的样本。模拟和真实的基因表达数据集的数值实验表明，iPcc可以大大突出潜在的模式噪声基因表达数据，从而大大提高了鲁棒性和准确性的算法，目前可用于疾病诊断和分类的基础上基因表达谱。
Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.
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影响因子：
24.5
作者：
Goh, X. Y.;Rees, J. R. E.;Fitzgerald, R. C.
通讯作者：
Fitzgerald, R. C.
影响因子：
30.8
作者：
Nair RP;Duffin KC;Helms C;Ding J;Stuart PE;Goldgar D;Gudjonsson JE;Li Y;Tejasvi T;Feng BJ;Ruether A;Schreiber S;Weichenthal M;Gladman D;Rahman P;Schrodi SJ;Prahalad S;Guthery SL;Fischer J;Liao W;Kwok PY;Menter A;Lathrop GM;Wise CA;Begovich AB;Voorhees JJ;Elder JT;Krueger GG;Bowcock AM;Abecasis GR;Collaborative Association Study of Psoriasis
通讯作者：
Collaborative Association Study of Psoriasis
DOI：
10.1073/pnas.97.1.262
发表时间：
2000-01-04
影响因子：
11.1
作者：
Brown, MPS;Grundy, WN;Haussler, D
通讯作者：
Haussler, D
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
3.8
作者：
MCQUITTY, LL
通讯作者：
MCQUITTY, LL