Interpool: interpreting smart-pooling results | Zendy

Nicolas ThierryMieg | Zendy; Gilles Bailly | Zendy

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Interpool: interpreting smart-pooling results

Author(s) -

Nicolas ThierryMieg,

Gilles Bailly

Publication year - 2008

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/btn001

Subject(s) - pooling , computer science , context (archaeology) , false positive paradox , throughput , set (abstract data type) , focus (optics) , noise (video) , decoding methods , theoretical computer science , data mining , machine learning , algorithm , artificial intelligence , programming language , paleontology , telecommunications , physics , image (mathematics) , optics , wireless , biology

In high-throughput projects aiming to identify rare positives using a binary assay, smart-pooling constitutes an appealing strategy liable of significantly reducing the number of tests while correcting for experimental noise. In order to perform simulations for choosing an appropriate set of pools, and later to interpret the experimental results, the pool outcomes must be 'decoded'. The intuitive aim is clearly to identify the positives that gave rise to an observation, whether real or simulated. However, this goal is not well-formalized and has been the focus of very few studies.

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