title: Active Learning Strategies for Phenotypic Profiling of High-Content Screens.
abstract: High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied.
authors.affiliation: Light Microscopy and Screening Centre, ETH Zurich, Switzerland.
authors.forename: Kevin
authors.initials: K
authors.lastname: Smith
authors.affiliation: Institute of Biochemistry, ETH Zurich, Switzerland Synthetic and Systems Biology Unit, Biological Research Center, Szeged, Hungary horvath.peter@brc.mta.hu.
authors.forename: Peter
authors.initials: P
authors.lastname: Horvath
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006367:HeLa Cells; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008853:Microscopy; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D010641:Phenotype; D018794:Problem-Based Learning; D012313:RNA / Q000737:chemistry; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000737:chemistry; D012270:Ribosomes / Q000737:chemistry; D012672:Semliki forest virus; D018066:Uukuniemi virus
chemical_list: D034741:RNA, Small Interfering; D012313:RNA
keywords: High-content screening; active learning; machine learning; multiparametric analysis; phenotypic discovery