Date of Award
Fall 1-2020
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computational and Data Sciences
First Advisor
Gennady Verkhivker
Second Advisor
Hesham El-Askary
Third Advisor
Erik Linstead
Fourth Advisor
Cyril Rakovski
Abstract
Few technological ideas have captivated the minds of biochemical researchers to the degree that machine learning (ML) and artificial intelligence (AI) have. Over the last few years, advances in the ML field have driven the design of new computational systems that improve with experience and are able to model increasingly complex chemical and biological phenomena. In this dissertation, we capitalize on these achievements and use machine learning to study drug receptor sites and design drugs to target these sites. First, we analyze the significance of various single nucleotide variations and assess their rate of contribution to cancer. Following that, we used a portfolio of machine learning and data science approaches to design new drugs to target protein kinase inhibitors. We show that these techniques exhibit strong promise in aiding cancer research and drug discovery.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
S. Agajanian, "Development of integrated machine learning and data science approaches for the prediction of cancer mutation and autonomous drug discovery of anti-cancer therapeutic agents," Ph.D. dissertation, Chapman University, Orange, CA, 2021. https://doi.org/10.36837/chapman.000220