Graphs are powerful structures able to capture topological and semantic information from data, hence suitable for modelling a plethora of real-world (complex) systems. For this reason, graph-based pattern recognition gained a lot of attention in recent years. In this paper, a general-purpose classification system in the graphs domain is presented. When most of the information of the available patterns can be encoded in edge labels, an information granulation-based approach is highly discriminant and allows for the identification of semantically meaningful edges. The proposed classification system has been tested on the entire set of organisms (5299) for which metabolic networks are known, allowing for both a perfect mirroring of the underlying taxonomy and the identification of most discriminant metabolic reactions and pathways. The widespread diffusion of graph (network) structures in biology makes the proposed pattern recognition approach potentially very useful in many different fields of application. More specifically, the possibility to have a reliable metric to compare different metabolic systems is instrumental in emerging fields like microbiome analysis and, more in general, for proposing metabolic networks as a universal phenotype spanning the entire tree of life and in direct contact with environmental cues.

译文

:Graphs是强大的结构,能够从数据中捕获拓扑和语义信息,因此适合于对大量现实世界(复杂)系统进行建模。因此,近年来,基于图形的模式识别备受关注。本文提出了一种图形领域的通用分类系统。当可用模式的大多数信息都可以在边缘标签中编码时,基于信息粒度的方法就具有很高的判别力,并且可以识别语义上有意义的边缘。拟议的分类系统已在已知代谢网络的整个有机体(5299)上进行了测试,既可以完美地反映基础分类学,又可以识别大多数判别性代谢反应和途径。图(网络)结构在生物学中的广泛传播使所提出的模式识别方法在许多不同的应用领域中非常有用。更具体地说,在微生物组分析等新兴领域,拥有可靠的度量标准来比较不同代谢系统的可能性非常重要,并且更广泛地说,对于将代谢网络作为跨越整个生命树并与环境线索直接接触的通用表型提出建议。

+1
+2
100研值 100研值 ¥99课程
检索文献一次
下载文献一次

去下载>

成功解锁2个技能,为你点赞

《SCI写作十大必备语法》
解决你的SCI语法难题!

技能熟练度+1

视频课《玩转文献检索》
让你成为检索达人!

恭喜完成新手挑战

手机微信扫一扫,添加好友领取

免费领《Endnote文献管理工具+教程》

微信扫码, 免费领取

手机登录

获取验证码
登录