Identifying HIV-induced subgraph patterns in brain networks with side information.
Identifying HIV-induced subgraph patterns in brain networks with side information.
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DOI：
10.1007/s40708-015-0023-1
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发表时间：
2015-12
影响因子：
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通讯作者：
Ragin AB
中科院分区：
文献类型：
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作者：
Cao B;Kong X;Zhang J;Yu PS;Ragin AB
近年来，研究用于神经系统疾病识别的大脑连接网络引起了人们的极大兴趣，其中大多数仅关注图形表示。然而，除了源自神经影像数据的大脑网络之外，还可以记录每个受试者的数百种临床、免疫学、血清学和认知测量。这些措施组成了多个侧面视图，编码了大量用于诊断目的的补充信息，但常常被忽视。在本文中，我们研究了具有侧信息引导的脑网络子图选择问题，并提出了一种新颖的解决方案，通过探索多个侧视图来找到用于图分类的最佳子图模式集。我们推导出一个名为 gSide 的特征评估标准，以根据侧视图估计子图模式的有用性。然后，我们开发了一种称为 gMSV 的分支定界算法，通过集成子图挖掘过程和判别性特征选择过程来有效地搜索最佳子图模式。使用脑网络对神经系统疾病的图分类任务进行的实证研究表明，通过多侧视图引导的子图选择方法选择的子图模式可以有效提高图分类性能，并且与疾病诊断相关。
Investigating brain connectivity networks for neurological disorder identification has attracted great interest in recent years, most of which focus on the graph representation alone. However, in addition to brain networks derived from the neuroimaging data, hundreds of clinical, immunologic, serologic, and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of subgraph selection from brain networks with side information guidance and propose a novel solution to find an optimal set of subgraph patterns for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph patterns by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view-guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.