NCI Data Jamboree (Project Abstract Submission): Submission #10

Presenter Information
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First Name: Morolake
Middle Initial: {Empty}
Last Name: Okanlawon
Degree(s): BSc.
Position/Title/Career Status: PhD Student
Organization: George Mason University
Organization Address:
Fairfax

Email: mokanlaw@gmu.edu

Additional Authors
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List of Additional Authors:
{Empty}


Abstract Information
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Abstract Category: Developing, refining, or validating tools, methods, algorithms, and pipelines
Abstract Keywords: machine learning, artificial intelligence, reproducible pipelines, model validation, cancer data integration
Abstract Title: Developing and Validating Machine Learning and AI Pipelines for Integrating and Analyzing Cancer Research Data
Abstract:
Reliable analysis of cancer research data depends not only on applying machine learning and artificial intelligence methods but on the tools and pipelines that make those methods reproducible and reusable. Building on prior experience employing statistical and computational methods to analyze data in my own research, this project focuses on developing, refining, and validating machine learning and AI pipelines for integrating and analyzing biomedical and cancer datasets. Proposed work includes constructing modular pipelines for data preprocessing, model training, and evaluation, along with validation procedures to assess reproducibility, robustness, and interpretability of AI models. Particular attention will be given to designing components that can be reused across clinical, genomic, or imaging datasets rather than tailored to a single analysis. As a PhD student with a background in Bioinformatics and Computational Biology,  I aim to extend my analytical foundation toward the development of machine learning and AI tools within a collaborative team. The anticipated outcome is a set of validated, reusable pipeline components that support reproducible machine learning and AI workflows for cancer research.