PEITH(Θ): perfecting experiments with information theory in Python with GPU support | Zendy

Leander Dony | Zendy; Jonas Mackerodt | Zendy; Scott Ward | Zendy; Sarah Filippi | Zendy; Michael P. H. Stumpf | Zendy; Juliane Liepe | Zendy

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

Author(s) -

Leander Dony,

Jonas Mackerodt,

Scott Ward,

Sarah Filippi,

Michael P. H. Stumpf,

Juliane Liepe

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx776

Subject(s) - python (programming language) , computer science , a priori and a posteriori , inference , bayesian probability , bayesian inference , approximate bayesian computation , theoretical computer science , artificial intelligence , programming language , epistemology , philosophy

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.

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