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For example, machine learning and text mining are significant components of computational bioinformatics that allow for connection of system elements (i.e., molecules) and modeling of networks of regulatory pathways.
These require simulators that support the modeling of networks and components with different levels of abstraction.
Although the evidence for our claim is persuasive, a more comprehensive understanding of the ACC's role depends upon future investigations with sophisticated modeling of networks, prospective and longitudinal designs, and examination of structure-function associations.
Network building tools available within Ingenuity were employed for bioinformatic modeling of networks [58], [59].
For statistical modeling of networks, exponential families of distributions offer robust and flexible parametric models [ 24].
For instance, PPIN data integrated with genome-wide expression profiles using DNA arrays and/or next generation sequencing enabling the modeling of networks have aided our understanding of how biological networks operate.
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This has led to a great interest among researchers in accurate modeling of network traffic.
Linear modeling of network dynamics has been available for some time.
In this work, we address the modeling of network traffic using a multi-time-scale framework.
In this paper, a statistical approach for the modeling of network intrusions as Markov processes is introduced.
Until recently, the large-scale modeling of network dynamics has been focused on individual network types.
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