{"id":17256770,"url":"https://github.com/le-ander/msc_bioinfo-experimental_design","last_synced_at":"2026-04-26T20:32:08.839Z","repository":{"id":171032443,"uuid":"80209584","full_name":"le-ander/MSc_Bioinfo-Experimental_Design","owner":"le-ander","description":"Using information theory to inform experimental design with GPU acceleration. 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Herein we present PEITH(Θ), a general\npurpose, command line interface built in Python, and developed to tackle the problem\nof experimental selection using information theory. PEITH(Θ) extends the work of Liepe\net al. [1] giving users the capability to simulate a range of experiments and make a\nselection beyond guesswork.\n\n## REFERENCES\n\n[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,\np. e1002888, 2013.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fle-ander%2Fmsc_bioinfo-experimental_design","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fle-ander%2Fmsc_bioinfo-experimental_design","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fle-ander%2Fmsc_bioinfo-experimental_design/lists"}