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Using information theory to inform experimental design with GPU acceleration. Computing group project as part of the MSc in Bioinformatics and Theorectical Systems Biology at Imperial College London 2016/2017.

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PEITHO - EXPERIMENTAL DESIGN

ABSTRACT

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. Herein we present PEITH(Θ), a general purpose, command line interface built in Python, and developed to tackle the problem of experimental selection using information theory. PEITH(Θ) extends the work of Liepe et al. [1] giving users the capability to simulate a range of experiments and make a selection beyond guesswork.

REFERENCES

[1] J. Liepe, S. Filippi, M. Komorowski, and M. P. Stumpf, “Maximizing the information content of experiments in systems biology,” PLoS Comput Biol, vol. 9, no. 1, p. e1002888, 2013.

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Using information theory to inform experimental design with GPU acceleration. Computing group project as part of the MSc in Bioinformatics and Theorectical Systems Biology at Imperial College London 2016/2017.

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