Protein networks tomography

E. Capobianco
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引用次数: 3

Abstract

Networks represent powerful inference tools for the analysis of complex biological systems. Inference is especially relevant when associations between network nodes are established by focusing on modularity. The problem of identifying first, and validating then, modules in networks has received substantial attention, and many approaches have been proposed. An important goal is functional validation of the identified modules, based on existing database resources. The quality and performance of algorithms can be assessed by evaluating the matching rate between retrieved and well annotated modules, in addition to newly established associations. Due to the variety of algorithms, the concept of module resolution spectrum has become central to this specific research field. In general, coarse-resolution modules reflect global network regulation patterns operating at the gene level or at the protein pathway scale. Fine-resolution modules localize dense regions, uncovering details of the variety of the constitutive connectivity patterns. The resolution limit problem is affected by uncertainty factors such as experimental accuracy and detection power of inference methods, and impacts the quality and accuracy of functional annotation. Our proposed approach works at the systems level; it aims to dissect networks and look at modularity in breadth-first search followed by in-depth analysis. In particular, “slicing” the protein interactome under exam yields a sort of tomography scan implemented by eigendecomposition of network affinity matrices. Such affinity matrices can be designed ad hoc, characterized by topological attributes, and analyzed with spectral methods. Consequently, a selected interactome data set allows the exploration of disease protein maps modularity through selected eigenmodes that are informative of both direct (protein-centric) and indirect (protein-neighbor centric) connectivity patterns of cancer targets and associated morbidities. The network tomography approach is thus recommended to infer about disease-induced multiscale modularity.
蛋白质网络断层扫描
网络是分析复杂生物系统的强大推理工具。当通过关注模块化来建立网络节点之间的关联时,推理尤其重要。首先识别网络中的模块并对其进行验证的问题受到了广泛的关注,并提出了许多方法。一个重要的目标是基于现有数据库资源对已识别模块进行功能验证。除了新建立的关联之外,还可以通过评估检索到的模块和注释良好的模块之间的匹配率来评估算法的质量和性能。由于算法的多样性,模块分辨率光谱的概念已经成为这一特定研究领域的核心。一般来说,粗分辨率模块反映了在基因水平或蛋白质途径尺度上运行的全球网络调控模式。精细分辨率模块定位密集区域,揭示各种本构连接模式的细节。解析极限问题受推理方法的实验精度和检测能力等不确定性因素的影响,影响功能标注的质量和准确性。我们建议的方法在系统层面起作用;它旨在剖析网络,并在深度分析之后查看广度优先搜索中的模块化。特别地,“切片”检查下的蛋白质相互作用组产生一种通过网络亲和矩阵的特征分解实现的断层扫描。这种亲和矩阵可以特别设计,用拓扑属性表征,并用谱方法分析。因此,选定的相互作用组数据集允许通过选定的特征模式探索疾病蛋白质图谱的模块化,这些特征模式提供了癌症靶点和相关发病率的直接(以蛋白质为中心)和间接(以蛋白质为中心)连接模式的信息。因此,网络断层扫描方法被推荐用于推断疾病引起的多尺度模块化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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