Gevaert O, Van Vooren S, de Moor B
Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven, B-3001, Belgium.
Pac Symp Biocomput. 2008:279-90. doi: 10.1142/9789812776136_0028.
Microarray data are notoriously noisy such that models predicting clinically relevant outcomes often contain many false positive genes. Integration of other data sources can alleviate this problem and enhance gene selection and model building. Probabilistic models provide a natural solution to integrate information by using the prior over model space. We investigated if the use of text information from PUBMED abstracts in the structure prior of a Bayesian network could improve the prediction of the prognosis in cancer. Our results show that prediction of the outcome with the text prior was significantly better compared to not using a prior, both on a well known microarray data set and on three independent microarray data sets.
微阵列数据的噪声非常大，以至于预测临床相关结果的模型通常包含许多假阳性基因。整合其他数据源可以缓解这个问题，并加强基因选择和模型构建。概率模型通过在模型空间上使用先验来提供一种整合信息的自然解决方案。我们研究了在贝叶斯网络的结构先验中使用来自PUBMED摘要的文本信息是否可以改善癌症预后的预测。我们的结果表明，在一个知名的微阵列数据集和三个独立的微阵列数据集上，使用文本先验对结果的预测明显优于不使用先验的情况。
