@article {Ando161422, author = {D. Michael Ando and Cory Y. McLean and Marc Berndl}, title = {Improving Phenotypic Measurements in High-Content Imaging Screens}, elocation-id = {161422}, year = {2017}, doi = {10.1101/161422}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Image-based screening is a powerful technique to reveal how chemical, genetic, and environmental perturbations affect cellular state. Its potential is restricted by the current analysis algorithms that target a small number of cellular phenotypes and rely on expert-engineered image features. Newer algorithms that learn how to represent an image are limited by the small amount of labeled data for ground-truth, a common problem for scientific projects. We demonstrate a sensitive and robust method for distinguishing cellular phenotypes that requires no additional ground-truth data or training. It achieves state-of-the-art performance classifying drugs by similar molecular mechanism, using a Deep Metric Network that has been pre-trained on consumer images and a transformation that improves sensitivity to biological variation. However, our method is not limited to classification into predefined categories. It provides a continuous measure of the similarity between cellular phenotypes that can also detect subtle differences such as from increasing dose. The rich, biologically-meaningful image representation that our method provides can help therapy development by supporting high-throughput investigations, even exploratory ones, with more sophisticated and disease-relevant models.}, URL = {https://www.biorxiv.org/content/early/2017/07/10/161422}, eprint = {https://www.biorxiv.org/content/early/2017/07/10/161422.full.pdf}, journal = {bioRxiv} }