Li Hai-Tao, Zhang Yu-Lang, Zheng Chun-Hou, Wang Hong-Qiang
College of Information and Communication Technology, Qufu Normal University, Rizhao 276826, China.
College of Jia Sixie Agriculture, Weifang University of Science and Technology, Shouguang 262700, China.
Biomed Res Int. 2014;2014:375980. doi: 10.1155/2014/375980. Epub 2014 May 26.
With the development of next-generation DNA sequencing technologies, large-scale cancer genomics projects can be implemented to help researchers to identify driver genes, driver mutations, and driver pathways, which promote cancer proliferation in large numbers of cancer patients. Hence, one of the remaining challenges is to distinguish functional mutations vital for cancer development, and filter out the unfunctional and random "passenger mutations." In this study, we introduce a modified method to solve the so-called maximum weight submatrix problem which is used to identify mutated driver pathways in cancer. The problem is based on two combinatorial properties, that is, coverage and exclusivity. Particularly, we enhance an integrative model which combines gene mutation and expression data. The experimental results on simulated data show that, compared with the other methods, our method is more efficient. Finally, we apply the proposed method on two real biological datasets. The results show that our proposed method is also applicable in real practice.
随着下一代DNA测序技术的发展，可以实施大规模癌症基因组学项目，以帮助研究人员识别驱动基因、驱动突变和驱动通路，这些因素促使大量癌症患者体内的癌细胞增殖。因此，剩下的挑战之一是区分对癌症发展至关重要的功能性突变，并过滤掉无功能的随机“乘客突变”。在本研究中，我们引入了一种改进方法来解决所谓的最大权重子矩阵问题，该问题用于识别癌症中的突变驱动通路。该问题基于两个组合属性，即覆盖性和排他性。特别地，我们增强了一个整合基因突变和表达数据的模型。在模拟数据上的实验结果表明，与其他方法相比，我们的方法更有效。最后，我们将所提出的方法应用于两个真实的生物学数据集。结果表明，我们提出的方法在实际应用中也是适用的。
