1. Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images
- Author
-
Urban, Gregor, Bache, Kevin, Phan, Duc TT, Sobrino, Agua, Shmakov, Alexander K, Hachey, Stephanie J, Hughes, Christopher CW, and Baldi, Pierre
- Subjects
Information and Computing Sciences ,Patient Safety ,Cancer ,Development of treatments and therapeutic interventions ,5.1 Pharmaceuticals ,Good Health and Well Being ,Antineoplastic Agents ,Cell Culture Techniques ,Deep Learning ,Drug Discovery ,Extracellular Matrix ,Humans ,Image Processing ,Computer-Assisted ,Microscopy ,Neoplasms ,Neovascularization ,Pathologic ,Neural Networks ,Computer ,Pattern Recognition ,Automated ,Machine learning ,computer vision ,Mathematical Sciences ,Biological Sciences ,Bioinformatics ,Biological sciences ,Information and computing sciences ,Mathematical sciences - Abstract
Likely drug candidates which are identified in traditional pre-clinical drug screens often fail in patient trials, increasing the societal burden of drug discovery. A major contributing factor to this phenomenon is the failure of traditional in vitro models of drug response to accurately mimic many of the more complex properties of human biology. We have recently introduced a new microphysiological system for growing vascularized, perfused microtissues that more accurately models human physiology and is suitable for large drug screens. In this work, we develop a machine learning model that can quickly and accurately flag compounds which effectively disrupt vascular networks from images taken before and after drug application in vitro. The system is based on a convolutional neural network and achieves near perfect accuracy while committing potentially no expensive false negatives.
- Published
- 2019