CSIRO, Data61, Eveleigh, NSW, Australia.
CSIRO, Environment, Brisbane, QLD, Australia.
Methods Mol Biol. 2024;2760:319-344. doi: 10.1007/978-1-0716-3658-9_19.
We briefly present machine learning approaches for designing better biological experiments. These approaches build on machine learning predictors and provide additional tools to guide scientific discovery. There are two different kinds of objectives when designing better experiments: to improve the predictive model or to improve the experimental outcome. We survey five different approaches for adaptive experimental design that iteratively search the space of possible experiments while adapting to measured data. The approaches are Bayesian optimization, bandits, reinforcement learning, optimal experimental design, and active learning. These machine learning approaches have shown promise in various areas of biology, and we provide broad guidelines to the practitioner and links to further resources.
我们简要介绍了用于设计更好的生物学实验的机器学习方法。这些方法建立在机器学习预测器的基础上，并提供了额外的工具来指导科学发现。在设计更好的实验时，有两种不同的目标：提高预测模型或改善实验结果。我们调查了五种不同的自适应实验设计方法，这些方法在适应测量数据的同时迭代搜索可能的实验空间。这些方法是贝叶斯优化、 强盗算法、 强化学习、 最优实验设计和主动学习。这些机器学习方法在生物学的各个领域都显示出了潜力，我们为从业者提供了广泛的指导方针，并提供了进一步的资源链接。
