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Big Data Analysis of Bacterial Inhibitors in Parallelized Cellomics - A Machine Learning Approach

Abstract Identifying chemical compounds that inhibit bacterial infection has recently gained a considerable amount of attention given the increased number of highly resistant bacteria and the serious health threat it poses around the world. With the development of automated microscopy and image analysis systems, the process of identifying novel therapeutic drugs can generate an immense amount of data - easily reaching terabytes worth of information. Despite increasing the vast amount of data that is currently generated, traditional analytical methods have not increased the overall success rate of identifying active chemical compounds that eventually become novel therapeutic drugs. Moreover, multispectral imaging has become ubiquitous in drug discove... (more)
Created Date 2016
Contributor Trevino, Robert (Author) / Liu, Huan (Advisor) / Lamkin, Thomas J (Committee member) / He, Jingrui (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Subject Computer science / Molecular biology / Convolution Neural Network / Feature Selection / High Content Screening / Machine Learning
Type Doctoral Dissertation
Extent 146 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis