Author ORCID Identifier
Date Available
4-27-2018
Year of Publication
2018
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College
Engineering
Department/School/Program
Computer Science
Advisor
Dr. Sally Ellingson
Co-Director of Graduate Studies
Dr. Nathan Jacobs
Abstract
In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in order to decrease the amount of expert knowledge required to model interactions between proteins and drug molecules.
Digital Object Identifier (DOI)
https://doi.org/10.13023/ETD.2018.137
Recommended Citation
Jones, Derek, "Scalable Feature Selection and Extraction with Applications in Kinase Polypharmacology" (2018). Theses and Dissertations--Computer Science. 65.
https://uknowledge.uky.edu/cs_etds/65
Included in
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Pharmaceutics and Drug Design Commons, Pharmacology Commons, Translational Medical Research Commons
