2024-03-19T13:13:32Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1388342022-12-20T08:53:22Zcom_10261_47com_10261_8col_10261_930
Villaverde, A. F.
Becker, Kolja
Banga, Julio R.
2016-10-21T11:30:04Z
2016-10-21T11:30:04Z
2016
Computational Methods in Systems Biology: 323-329 (2016)
978-3-319-45176-3
978-3-319-45177-0
http://hdl.handle.net/10261/138834
10.1007/978-3-319-45177-0_21
http://dx.doi.org/10.13039/501100000780
http://dx.doi.org/10.13039/501100003329
http://dx.doi.org/10.13039/501100002347
http://dx.doi.org/10.13039/501100010801
A common approach for reverse engineering biological networks from data is to deduce the existence of interactions among nodes from information theoretic measures. Estimating these quantities in a multidimensional space is computationally demanding for large datasets. This hampers the application of elaborate algorithms – which are crucial for discarding spurious interactions and determining causal relationships – to large-scale network inference problems. To alleviate this issue we have developed PREMER, a software tool which can automatically run in parallel and sequential environments, thanks to its implementation of OpenMP directives. It recovers network topology and estimates the strength and causality of interactions using information theoretic criteria, and allowing the incorporation of prior knowledge. A preprocessing module takes care of imputing missing data and correcting outliers if needed. PREMER (https://sites.google.com/site/premertoolbox/) runs on Windows, Linux and OSX, it is implemented in Matlab/Octave and Fortran 90, and it does not require any commercial software
eng
openAccess
Network inference
Information theory
Parallel computing
PREMER: parallel reverse engineering of biological networks with information theory
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