Inference of large-scale topology of gene regulation networks by neural nets
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چکیده
* 0-7803-7952-7/03/$17.00 2003 IEEE. Abstract This paper addresses the problem of inferring topological features of gene regulation networks from data that are likely to be available from current experimental methods, such as DNA microarrays. The proposed method uses neural networks to predict the topology class from histograms of perturbation propagation data. The preliminary results with simulated data are encouraging. The trained neural network is able to classify the network topology as random (exponential) or scale-free with 90% accuracy. Compare to the previous network connectivity inference methods that are often problematic with current noisy data, this method is expected to be more robust because it uses global characteristics of dynamic networks.
منابع مشابه
Title : Inference of gene regulation network topology by perturbation analysis
This paper addresses the problem of identifying the large-scale topology of gene regulation networks from features that can be derived from microarray data sets. Understanding large-scale structures of gene regulation is fundamentally important in biology. Three main classes of network models, exponential network, scale-free network, and small-world network, have been used to describe topologic...
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تاریخ انتشار 2003