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PLoS One. 2013 Dec 17;8(12):e83996. doi: 10.1371/journal.pone.0083996. eCollection 2013.

Efficient modeling and active learning discovery of biological responses.

Author information

1
Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
2
Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America ; Departments of Biological Sciences, Biomedical Engineering and Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America ; Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, Freiburg, Germany.

Abstract

High throughput and high content screening involve determination of the effect of many compounds on a given target. As currently practiced, screening for each new target typically makes little use of information from screens of prior targets. Further, choices of compounds to advance to drug development are made without significant screening against off-target effects. The overall drug development process could be made more effective, as well as less expensive and time consuming, if potential effects of all compounds on all possible targets could be considered, yet the cost of such full experimentation would be prohibitive. In this paper, we describe a potential solution: probabilistic models that can be used to predict results for unmeasured combinations, and active learning algorithms for efficiently selecting which experiments to perform in order to build those models and determining when to stop. Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random.

PMID:
24358322
PMCID:
PMC3866149
DOI:
10.1371/journal.pone.0083996
[Indexed for MEDLINE]
Free PMC Article
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