For this purpose, we estimated a probabilistic network of relationships between these genes using a Bayesian network estimation program, SiGN-BN [ 9, 10], implemented on the supercomputer system at Human Genome Center, The Institute of Medical Science, The University of Tokyo [ 11].
For a neural network estimation of certain values, it is common to have a difference between the actual output and the estimated values.
To overcome such a limitation, Hirose et al. [ 12] proposed a module-based gene network estimation which explores genetic networks of the transcriptional modules which are sets of genes sharing a common function or involved in the same path way rather than the use of gene level networks.
A potential hazardous station was identified from the neural network estimation, which exhibited a significantly higher acceleration than that of the design value.
Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology.
This scheme has been evolved from the traditional single agent virtual screening method which relies on evaluating binding affinity of the agent to single target, on the other hand, this model has focused on a network screening strategy based on phenotypic data of drug molecules against a complex disease by general network estimation.
To elucidate effects of various biological characteristics on network estimation accuracy, we performed a systematic simulation experiment with an ensemble of systems genetics data where each parameter (network characteristic) was sampled over a biologically plausible range.
In addition, another artificial neural network estimation approach with a single back-propagation network, called FAPSO-SBP, is also established.
Finally, the team will take a data-driven approach to the network estimation based on semi-supervised representation learning.
PriorPC uses prior knowledge to exclude unlikely edges from network estimation and introduces a particular ordering for the conditional independence tests.
