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PEITH(Theta): perfecting experiments with information theory in Python with GPU support

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Title: PEITH(Theta): perfecting experiments with information theory in Python with GPU support
Authors: Dony, L
Mackerodt, J
Ward, S
Filippi, S
Stumpf, MPH
Liepe, J
Item Type: Journal Article
Abstract: Motivation Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial. Results PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions. Availability and implementation: https://github.com/MichaelPHStumpf/Peitho
Issue Date: 1-Apr-2018
Date of Acceptance: 4-Dec-2017
URI: http://hdl.handle.net/10044/1/60281
DOI: https://dx.doi.org/10.1093/bioinformatics/btx776
ISSN: 1367-4803
Publisher: Oxford University Press (OUP)
Start Page: 1249
End Page: 1250
Journal / Book Title: Bioinformatics
Volume: 34
Issue: 7
Copyright Statement: © 2017 The Author. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Science & Technology
Life Sciences & Biomedicine
Technology
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Mathematics
APPROXIMATE BAYESIAN COMPUTATION
SYSTEMS
DESIGN
01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
Bioinformatics
Publication Status: Published
Open Access location: https://academic.oup.com/bioinformatics/article/34/7/1249/4708231
Online Publication Date: 2017-12-07
Appears in Collections:School of Public Health
Faculty of Natural Sciences