标题： 使用生成库模型在大规模屏幕上进行加速学习 显示英文标题

标题： Accelerated Learning on Large Scale Screens using Generative Library Models

摘要： 生物机器学习通常受到数据规模不足的瓶颈限制。 缓解数据瓶颈的一种有前景的方法是通过高通量筛选，这可以在实验中并行测试$10^6-10^{12}$个蛋白质序列的活性。 在本文中，我们介绍了优化高通量筛选以进行数据创建和模型训练的算法。 我们关注大规模情况，其中数据集大小受到测量和测序成本的限制。 我们表明，当活性序列很少时，如果我们仅收集活性序列的正例，即$x$具有$y>0$，则可以最大化信息增益。 我们可以使用文库的生成模型来纠正缺失的负例，从而对真实的$p(y | x)$进行一致且高效的估计。 我们在模拟和大规模抗体筛选中展示了这种方法。 总体而言，实验与推理的协同设计使我们能够显著加速学习。 显示英文摘要

摘要： Biological machine learning is often bottlenecked by a lack of scaled data. One promising route to relieving data bottlenecks is through high throughput screens, which can experimentally test the activity of $10^6-10^{12}$ protein sequences in parallel. In this article, we introduce algorithms to optimize high throughput screens for data creation and model training. We focus on the large scale regime, where dataset sizes are limited by the cost of measurement and sequencing. We show that when active sequences are rare, we maximize information gain if we only collect positive examples of active sequences, i.e. $x$ with $y>0$. We can correct for the missing negative examples using a generative model of the library, producing a consistent and efficient estimate of the true $p(y | x)$. We demonstrate this approach in simulation and on a large scale screen of antibodies. Overall, co-design of experiments and inference lets us accelerate learning dramatically.