基于融合信息的癌症相关基因选择方法

Cancer Relevant Genes Selection Approach from Integrated Information

基因表达数据的出现,为人类从分子生物学的角度研究和探索癌症的发病机理提供了广阔的前景,利用基因表达数据发现与癌症相关的基因对于癌症的诊断和治疗具有重要的意义。在过去的十几年里,已经有很多种计算方法被成功地用于从基因表达数据中找出与癌症相关的关键基因,然而,不同的方法从不同的角度刻画基因对不同类型样本的区分能力,它们选择出来的关键基因可能不一致,这将给医学解释和应用带来困扰。现提出一种融合的方法,即将基因在不同方面对样本的判别能力结合起来,首先计算每个基因的信息增益、全局判别能力和局部判别能力,再用它们的识别率进行加权,进而计算每个基因的综合判别能力,最后筛选出判别能力最高的基因子集作为关键基因子集。实验结果表明,此方法得到了比采用单独一种评价标准更好的识别效果。

With the advent of gene expression data,it has shown great promise to explore cancer pathogenesis at the level of molecular biology.Exploring the key genes related with carcinoma is of great importance to the cancer diagnosis and treatment.During the past few decades,many approaches were successfully proposed to select the key genes related with carcinoma.However,the gene sets selected by different methods are not always consistent with each other,this will cause difficulty to biological interpretation and practical application.A novel approach based on integrated information was proposed for the selection of key genes from gene expression data in this study,the three kinds of information,namely information gain,global discriminate ability and local discriminate ability were derived firstly,then they were weighted and integrated into a score to characterize the discriminate ability of each gene,and the gene set with the highe-st classification accuracy was selected as key gene set.The experimental results showed that our approach can get better performance than those based on single information.