基于基因表达谱的肿瘤分型和特征基因选取

在分析基因表达谱数据特性的基础上,提出了一个将之用于肿瘤分子分型和选型和选取相应亚型特征基因的策略。该策略包括三个步骤：首先采用一个无监督的基因过滤算法以降低用于分型计算的数据的噪声,其次提出了一个概率模型对样本中的分类结构进行建模,最后基于聚类的结果采用相对熵的方法获得对分类贡献大的基因作为特征基因,应用该策略对两个公开发表的数据集进行了再挖掘,结果表明不但获得了其他方法可以得到的信息,而且还提供了更精细、更具有显著生物学意义的信息,具有明显的优越性。

CANCER SUBTYPE DISCOVERY AND INFORMATIVE GENE IDENTIFICATION WITH GENE EXPRESSION PROFILES

Because of their significance in cancer diagnosis and treatment, discovering new subtypes within cancers and identifying the characteristically expressed genes for each subtype based on gene ex-pression data analysis become more and more popular in recent research. A major challenge to find new subtypes is to eliminate noise, such that the newly discovered subtypes are biologically relevant and not related to experimental conditions. Several algorithms were presented in the literature but most of them gave insufficient consideration to this problem. An unsupervised filtering method to remove the "noise-genes" firstly, and then the samples are clus-tered using Bayesian Finite Mixture Model, finally the characteristically expressed genes in each subtypes are identified based on the clustering results by means of relative entropy. This process was applied to two data sets, one from an oligonucleotide gene chip experiment for leukemia samples, and the other from a cDNA microarray experiment for lymphoma samples. The discoveries have very explicit biological meanings.