BioCode：一种数据驱动的生物网络增长学习程序
BioCode: A Data-Driven Procedure to Learn the Growth of Biological Networks
AI总结：
BioCode是一个数据驱动框架，通过基本指令和遗传算法自动发现生物网络增长模型，在合作、基因调控、代谢和蛋白质网络中生成与真实特征匹配的模型。
摘要：
概率性生物网络增长模型已被用于许多任务，包括但不限于捕捉生物增长活动的机制和动态、零模型表示、捕捉异常等。这些概率模型的著名例子包括Kronecker模型、优先连接模型和基于复制的模型。然而，随着新网络的不断观察，我们应经常开发新模型以更好地拟合和解释观察到的网络特征。此外，每次研究新网络时，开发一个增长模型是困难的。在本文中，我们提出了BioCode，一个自动发现匹配用户指定图属性的新型生物增长模型的框架，适用于有向和无向生物图。BioCode设计了一组基本指令，这些指令足够通用，可以模拟许多已知的生物图增长模型。我们将这种基于指令的表示与基于遗传算法的优化程序相结合，为各种生物网络编码模型。我们主要评估BioCode在发现生物合作网络、基因调控网络、代谢网络和蛋白质相互作用网络模型方面的性能，这些网络的特征如同配性、聚类系数、度分布与相应真实生物网络中的真实特征紧密匹配。如模拟图上的测试所示，由BioCode生成的生物网络分布的方差与已知模型对这些生物网络类型的方差相似。
英文摘要：
Probabilistic biological network growth models have been utilized for many tasks including but not limited to capturing mechanism and dynamics of biological growth activities, null model representation, capturing anomalies, etc. Well-known examples of these probabilistic models are Kronecker model, preferential attachment model, and duplication-based model. However, we should frequently keep developing new models to better fit and explain the observed network features while new networks are being observed. Additionally, it is difficult to develop a growth model each time we study a new network. In this paper, we propose BioCode, a framework to automatically discover novel biological growth models matching user-specified graph attributes in directed and undirected biological graphs. BioCode designs a basic set of instructions which are common enough to model a number of well-known biological graph growth models. We combine such instruction-wise representation with a genetic algorithm based optimization procedure to encode models for various biological networks. We mainly evaluate the performance of BioCode in discovering models for biological collaboration networks, gene regulatory networks, metabolic networks, and protein interaction networks which features such as assortativity, clustering coefficient, degree distribution closely match with the true ones in the corresponding real biological networks. As shown by the tests on the simulated graphs, the variance of the distributions of biological networks generated by BioCode is similar to the known models' variance for these biological network types.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
