Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images
| Autor: | Duc T. T. Phan, Alexander Shmakov, Stephanie J. Hachey, Christopher C.W. Hughes, Agua Sobrino, Gregor Urban, Kevin Bache, Pierre Baldi |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
0206 medical engineering Cell Culture Techniques Antineoplastic Agents 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Article Pattern Recognition Automated Deep Learning Neoplasms Drug Discovery Genetics Drug response Image Processing Computer-Assisted Humans In patient Microscopy Artificial neural network Neovascularization Pathologic business.industry Drug discovery Drug screens Applied Mathematics Deep learning Human physiology Extracellular Matrix Artificial intelligence Neural Networks Computer business computer 020602 bioinformatics Biotechnology |
| Zdroj: | IEEE/ACM Trans Comput Biol Bioinform |
| ISSN: | 1557-9964 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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