Modified logistic regression models using gene coexpression and clinical features to predict prostate cancer progression.
Modified logistic regression models using gene coexpression and clinical features to predict prostate cancer progression.
复制标题
使用基因共表达和临床特征来预测前列腺癌进展的修改逻辑回归模型。
DOI：
10.1155/2013/917502
复制
发表时间：
2013
影响因子：
--
通讯作者：
Dai,Jianguo
中科院分区：
文献类型：
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作者：
Zhao,Hongya;Logothetis,ChristopherJ;Gorlov,IvanP;Zeng,Jia;Dai,Jianguo
预测疾病进展是前列腺癌研究中最具挑战性的问题之一。将基因表达数据添加到基于临床特征的预测模型中可以提高准确性。在本研究中，我们采用了结合临床特征和基因共表达数据的逻辑回归（LR）模型来提高前列腺癌进展预测的准确性。采用最高得分对（TSP）法选择模型基因。该模型既保留了TSP算法的基本特性，又将临床特征纳入了预后模型。基于迭代交叉验证的统计推断，我们证明了包含TSP方法选择的基因的预测LR模型比仅使用临床变量和/或包含单基因一次方法选择的基因的预测LR模型更能预测前列腺癌的进展。因此，我们得出结论，TSP选择是用于预后模型的特征（和/或基因）选择的有用工具，我们的模型也为预测前列腺癌进展提供了另一种选择。
Predicting disease progression is one of the most challenging problems in prostate cancer research. Adding gene expression data to prediction models that are based on clinical features has been proposed to improve accuracy. In the current study, we applied a logistic regression (LR) model combining clinical features and gene co‐expression data to improve the accuracy of the prediction of prostate cancer progression. The top‐scoring pair (TSP) method was used to select genes for the model. The proposed models not only preserved the basic properties of the TSP algorithm but also incorporated the clinical features into the prognostic models. Based on the statistical inference with the iterative cross validation, we demonstrated that prediction LR models that included genes selected by the TSP method provided better predictions of prostate cancer progression than those using clinical variables only and/or those that included genes selected by the one‐gene‐at‐a‐time approach. Thus, we conclude that TSP selection is a useful tool for feature (and/or gene) selection to use in prognostic models and our model also provides an alternative for predicting prostate cancer progression.