NemoProfile：一种通过实例收集进行网络基序分析的有效方法。
NemoProfile as an efficient approach to network motif analysis with instance collection.
作者信息
Kim Wooyoung, Haukap Lynnette
机构信息
Division of Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics (STEM), University of Washington Bothell, 18115 Campus Way NE, Bothell, 98011-8246, WA, USA.
出版信息
BMC Bioinformatics. 2017 Oct 16;18(Suppl 12):423. doi: 10.1186/s12859-017-1822-6.
BACKGROUND
A network motif is defined as a statistically significant and recurring subgraph pattern within a network. Most existing instance collection methods are not feasible due to high memory usage issues and provision of limited network motif information. They require a two-step process that requires network motif identification prior to instance collection. Due to the impracticality in obtaining motif instances, the significance of their contribution to problem solving is debated within the field of biology.
RESULTS
This paper presents NemoProfile, an efficient new network motif data model. NemoProfile simplifies instance collection by resolving memory overhead issues and is seamlessly generated, thus eliminating the need for costly two-step processing. Additionally, a case study was conducted to demonstrate the application of network motifs to existing problems in the field of biology.
CONCLUSION
NemoProfile comprises network motifs and their instances, thereby facilitating network motifs usage in real biological problems.
背景
网络模体被定义为网络中具有统计显著性且反复出现的子图模式。由于高内存使用问题以及提供的网络模体信息有限，大多数现有的实例收集方法不可行。它们需要一个两步过程，即在实例收集之前需要进行网络模体识别。由于获取模体实例不切实际，其对解决问题的贡献在生物学领域存在争议。
结果
本文提出了NemoProfile，一种高效的新网络模体数据模型。NemoProfile通过解决内存开销问题简化了实例收集，并且是无缝生成的，从而消除了昂贵的两步处理的需要。此外，进行了一个案例研究以证明网络模体在生物学领域现有问题中的应用。
结论
NemoProfile包含网络模体及其实例，从而便于在实际生物学问题中使用网络模体。
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