Predicting cytotoxicity from heterogeneous data sources with Bayesian learning

@inproceedings{Langdon2010PredictingCF,
  title={Predicting cytotoxicity from heterogeneous data sources with Bayesian learning},
  author={Sarah R. Langdon and Joanna Mulgrew and Gaia Valeria Paolini and Willem P. van Hoorn},
  booktitle={J. Cheminformatics},
  year={2010}
}
BACKGROUND We collected data from over 80 different cytotoxicity assays from Pfizer in-house work as well as from public sources and investigated the feasibility of using these datasets, which come from a variety of assay formats (having for instance different measured endpoints, incubation times and cell types) to derive a general cytotoxicity model. Our main aim was to derive a computational model based on this data that can highlight potentially cytotoxic series early in the drug discovery… CONTINUE READING
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