一种二进制癌症单驱动通路识别模型和算法

Binary cancer single-driver pathway identification model and algorithm

针对驱动通路识别的相关研究依赖传统生物实验方法,存在费时费力且经济成本高的问题,提出一种新的二进制癌症驱动通路识别方法PEA-BLMWS。首先,利用已有的基因表达数据,通过对比正常基因与突变基因表达量的差异,挖掘潜在的基因突变数据;其次,引入蛋白质相互作用网络数据,构建出一个改进的二进制线性最大权重子矩阵模型;最后,提出一种双亲协同进化算法求解该矩阵模型。在GBM(glioblastoma)和OVCA(ovarian cancer)数据集上的实验结果表明,相比于其他先进的Dendrix、CCA-NMWS和CGP-NCM识别方法,PEA-BLMWS识别的基因集中有更多基因富集在已知的信号通路中,未富集在信号通路中的基因也与癌症的发生密切相关,故该识别方法可作为一种驱动通路识别的有效工具。

The researches on driver pathway identification in cancer rely on traditional biological experiments,which have the drawbacks of being time-consuming,labor-intensive and costly.This paper proposed a novel binary cancer driver pathway identification method called PEA-BLMWS(parental evolutionary algorithm-binary linear maximum weight sub-matrix).Firstly,it utilized the existing gene expression data to uncovered potential gene mutation data by comparing the differences in expression levels between normal and mutated genes.Secondly,it incorporated protein-protein interaction network data to construct an improved binary linear maximum weight sub-matrix model.Finally,it proposed a parental evolutionary algorithm to solve this matrix model.Experimental results on the GBM(glioblastoma)and OVCA(ovarian cancer)datasets show that compared to other advanced identification methods such as Dendrix,CCA-NMWS and CGP-NCM,the gene set identified by PEA-BLMWS has more genes enriched in known signaling pathways,and genes not enriched in signaling pathways are also closely related to the occurrence of cancer.Therefore,this identification method can serve as an effective tool for driving pathway identification.