Efficient Biological Data Acquisition through Inference Set Design

TL;DR

This paper introduces a confidence-based active learning method called inference set design to efficiently select a subset of compounds for biological experiments, reducing costs while maintaining high accuracy.

Contribution

It proposes a novel inference set design approach that selectively acquires labels for the hardest examples to improve overall system performance in biological data acquisition.

Findings

Significant reduction in experimental costs demonstrated.

High system performance maintained with fewer experiments.

Effective on image, molecular datasets, and real-world biological assays.

Abstract

In drug discovery, highly automated high-throughput laboratories are used to screen a large number of compounds in search of effective drugs. These experiments are expensive, so one might hope to reduce their cost by only experimenting on a subset of the compounds, and predicting the outcomes of the remaining experiments. In this work, we model this scenario as a sequential subset selection problem: we aim to select the smallest set of candidates in order to achieve some desired level of accuracy for the system as a whole. Our key observation is that, if there is heterogeneity in the difficulty of the prediction problem across the input space, selectively obtaining the labels for the hardest examples in the acquisition pool will leave only the relatively easy examples to remain in the inference set, leading to better overall system performance. We call this mechanism inference set design, and propose the use of a confidence-based active learning solution to prune out these challenging examples. Our algorithm includes an explicit stopping criterion that interrupts the acquisition loop when it is sufficiently confident that the system has reached the target performance. Our empirical studies on image and molecular datasets, as well as a real-world large-scale biological assay, show that active learning for inference set design leads to significant reduction in experimental cost while retaining high system performance.