Abstract
Abstract In the last decade deep learning has had a very large impact in the field of image analysis. In this talk, I will discuss several vignettes into how deep learning when applied to biomedical imaging datasets can provide us with insights that can further drug discovery campaigns and uncover new biological insights. In particular, I will show how deep learning can learn a phenotypic manifold that allows us to interrogate the dose response behavior of compounds, measure the phenotypic similarity of compounds, and deconvolute MOAs of new compounds. Citation Format: Mohammad Muneeb Sultan. Machine learning enabled analysis of high content imaging datasets: Progress and prospects [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-10.
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