arXiv 1504.024062015-04-09q-bio.QMcs.LGstat.ML

决定何时停止：有效停止主动学习指导的药物靶点预测

Deciding when to stop: Efficient stopping of active learning guided drug-target prediction

Freiburg Institute for Advanced Studies, University of Freiburg（弗赖堡大学弗赖堡高等研究院）；Carnegie Mellon University（卡内基梅隆大学）

Maja Temerinac-Ott, Armaghan W. Naik, Robert F. Murphy

备注：: This paper was selected for oral presentation at RECOMB 2015 and an abstract is published in the conference proceedings

摘要：

主动学习已经证明可以减少获得高置信度药物靶点预测所需的实验数量。然而，为了使用主动学习实际保存实验，关键是要有一种方法来评估当前预测的质量并决定何时停止实验过程。只有将可靠的停止准则应用于主动学习，才能真正节省实验过程中的时间和成本。我们在模拟药物-目标矩阵上计算主动学习痕迹，以便学习主动学习者准确性的回归模型。通过分析回归模型在模拟数据上的性能，我们设计了以前看不见的实验矩阵的停止准则。我们在四个先前表征的药物效应数据集上证明，应用停止标准可以节省高达40%的总实验，以实现高度准确的预测。

英文摘要：

Active learning has shown to reduce the number of experiments needed to obtain high-confidence drug-target predictions. However, in order to actually save experiments using active learning, it is crucial to have a method to evaluate the quality of the current prediction and decide when to stop the experimentation process. Only by applying reliable stoping criteria to active learning, time and costs in the experimental process can be actually saved. We compute active learning traces on simulated drug-target matrices in order to learn a regression model for the accuracy of the active learner. By analyzing the performance of the regression model on simulated data, we design stopping criteria for previously unseen experimental matrices. We demonstrate on four previously characterized drug effect data sets that applying the stopping criteria can result in upto 40% savings of the total experiments for highly accurate predictions.

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