A primary challenge facing modern scientific research is the limited
availability of gold-standard data which can be both costly and labor-intensive
to obtain. With the rapid development of machine learning (ML), scientists have
relied on ML algorithms to predict these gold-standard outcomes with easily
obtained covariates. However, these predicted outcomes are often used directly
in subsequent statistical analyses, ignoring imprecision and heterogeneity
introduced by the prediction procedure. This will likely result in false
positive findings and invalid scientific conclusions. In this work, we
introduce an assumption-lean and data-adaptive Post-Prediction Inference
(POP-Inf) procedure that allows valid and powerful inference based on
ML-predicted outcomes. Its "assumption-lean" property guarantees reliable
statistical inference without assumptions on the ML-prediction, for a wide
range of statistical quantities. Its "data-adaptive'" feature guarantees an
efficiency gain over existing post-prediction inference methods, regardless of
the accuracy of ML-prediction. We demonstrate the superiority and applicability
of our method through simulations and large-scale genomic data.

这项研究介绍了一种无假设和数据自适应的后预测推断（POP-Inf）过程，能够基于机器学习预测的结果进行有效和有力的推断，并展示了其优越性和适用性通过模拟和大规模基因组数据。

基于数据自适应的假设简化和预测后推理

Assumption-lean and Data-adaptive Post-Prediction Inference

Despite an explosion in the number of experimentally determined, atomically
detailed structures of biomolecules, many critical tasks in structural biology
remain data-limited. Whether performance in such tasks can be improved by using
large repositories of tangentially related structural data remains an open
question. To address this question, we focused on a central problem in biology:
predicting how proteins interact with one another---that is, which surfaces of
one protein bind to those of another protein. We built a training dataset, the
Database of Interacting Protein Structures (DIPS), that contains biases but is
two orders of magnitude larger than those used previously. We found that these
biases significantly degrade the performance of existing methods on
gold-standard data. Hypothesizing that assumptions baked into the hand-crafted
features on which these methods depend were the source of the problem, we
developed the first end-to-end learning model for protein interface prediction,
the Siamese Atomic Surfacelet Network (SASNet). Using only spatial coordinates
and identities of atoms, SASNet outperforms state-of-the-art methods trained on
gold-standard structural data, even when trained on only 3% of our new dataset.
Code and data available at this https URL

本研究基于一个大型数据库，通过开发学习模型 SASNet，改进了蛋白质相互作用预测的方法，证明了现有模型的性能不足以适用于大尺度数据，新模型展现出更好的性能。