GreenSim: A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference
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/pubs-software
@InProceedings{fogelbergPalade2010a, author = {Christopher Fogelberg and Vasile Palade}, title = {GreenSim: A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference}, booktitle = {IEEE ICTAI2010}, pages = {225--230}, year = 2010, volume = 2, address = {Arras, France}, month = {October}, publisher = {IEEE Computer Society}, keywords = {greensim, simulation, evaluation, machine learning, regulatory network, grn} }
Available here.