Active machine learning-driven experimentation to determine compound effects on protein patterns.

@article{Naik2016ActiveML,
  title={Active machine learning-driven experimentation to determine compound effects on protein patterns.},
  author={Armaghan W. Naik and Joshua D Kangas and Devin P. Sullivan and Robert F. Murphy},
  journal={eLife},
  year={2016},
  volume={5},
  pages={e10047}
}
High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many… CONTINUE READING