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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >A Network-Based Comparison Between Molecular Apocrine Breast Cancer Tumor and Basal and Luminal Tumors by Joint Graphical Lasso
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A Network-Based Comparison Between Molecular Apocrine Breast Cancer Tumor and Basal and Luminal Tumors by Joint Graphical Lasso

机译：通过联合图形套索分子组分乳腺癌肿瘤和基础和腔肿瘤之间的基于网络的比较

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摘要

Joint graphical lasso (JGL) approach is a Gaussian graphical model to estimate multiple graphical models corresponding to distinct but related groups. Molecular apocrine (MA) breast cancer tumor has similar characteristics to luminal and basal subtypes. Due to the relationship between MA tumor and two other subtypes, this paper investigates the similarities and differences between the MA genes association network and the ones corresponding to other tumors by taking advantageous of JGL properties. Two distinct JGL graphical models are applied to two sub-datasets including the gene expression information of the MA and the luminal tumors and also the MA and the basal tumors. Then, topological comparisons between the networks such as finding the shared edges are applied. In addition, several support vector machine (SVM) classification models are performed to assess the discriminating power of some critical nodes in the networks, like hub nodes, to discriminate the tumors sample. Applying the JGL approach prepares an appropriate tool to observe the networks of the MA tumor and other subtypes in one map. The results obtained by comparing the networks could be helpful to generate new insight about MA tumor for future studies.
机译：联合图形套索（JGL）方法是高斯图形模型，用于估计与不同但相关群体相对应的多个图形模型。分子组分（MA）乳腺癌肿瘤对腔和基底亚型具有相似的特征。由于MA肿瘤和另外两种亚型之间的关系，本文通过采用JGL性质来研究MA基因关联网络与对应于其他肿瘤的相似性和差异。将两个不同的JGL图形模型应用于两个子数据集，包括MA和腔瘤的基因表达信息以及MA和基础肿瘤。然后，应用诸如查找共享边缘的网络之间的拓扑比较。另外，执行几种支持向量机（SVM）分类模型以评估网络中某些关键节点的区别力，如轮毂节点，以区分肿瘤样本。应用JGL方法准备了一个适当的工具，以在一个地图中观察MA肿瘤和其他亚型的网络。通过比较网络获得的结果可能有助于为未来的研究产生关于MA肿瘤的新洞察力。

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