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PREMER: parallel reverse engineering of biological networks with information theory

AutorVillaverde, A. F. ; Becker, Kolja; Banga, Julio R.
Palabras claveNetwork inference
Information theory
Parallel computing
Fecha de publicación2016
EditorSpringer
CitaciónComputational Methods in Systems Biology: 323-329 (2016)
SerieLecture Notes in Computer Science, v. 9859
ResumenA 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
Descripción7 páginas, 3 figuras.-- 14th International Conference, CMSB 2016, Cambridge, UK, September 21-23, 2016, Proceedings
Versión del editorhttp://dx.doi.org/10.1007/978-3-319-45177-0_21
URIhttp://hdl.handle.net/10261/138834
DOI10.1007/978-3-319-45177-0_21
ISBN978-3-319-45176-3
Referencias978-3-319-45177-0
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