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- W2071013590 abstract "Networks are very important in many fields of machine learning research. Within networks research, inferring the structure of unknown networks is often a key problem; e.g. of genetic regulatory networks. However, there are very few well-known biological networks, and good simulation is essential for validating and evaluating novel structural inference techniques. Further, the importance of large, genome-wide structural inference is increasingly recognised, but there does not appear to be a good simulator available for large networks. This paper presents GreenSim, a simulator that helps address this gap. GreenSim automatically generates large, genome-size networks with more biologically realistic structural characteristics and 2nd-order non-linear regulatory functions. The simulator itself and the novel method used for generating a network structure with appropriate in- and out-degree distributions may also generalise easily to other types of network. GreenSim is available online at: http://syntilect.com/cgf/pubs:software." @default.
- W2071013590 created "2016-06-24" @default.
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- W2071013590 date "2010-10-01" @default.
- W2071013590 modified "2023-10-17" @default.
- W2071013590 title "GreenSim: A Network Simulator for Comprehensively Validating and Evaluating New Machine Learning Techniques for Network Structural Inference" @default.
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- W2071013590 doi "https://doi.org/10.1109/ictai.2010.105" @default.
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