Development of Integrated Machine Learning and Data Science Approaches for the Prediction of Cancer Mutation and Autonomous Drug Discovery of Anti-Cancer Therapeutic Agents

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year

2020

Author dc:creator

• Agajanian, Steven

Contributors dc:contributor

• Gennady Verkhivker
• Hesham El-Askary
• Erik Linstead
• Cyril Rakovski

• Machine Learning
• Data Science
• Bioinformatics
• Cancer
• Drug Discovery

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/17

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1017