2024-03-19T12:59:46Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1388342022-12-20T08:53:22Zcom_10261_47com_10261_8col_10261_930
00925njm 22002777a 4500
dc
Villaverde, A. F.
author
Becker, Kolja
author
Banga, Julio R.
author
2016
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
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
Network inference
Information theory
Parallel computing
PREMER: parallel reverse engineering of biological networks with information theory