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Developing an Inverse Reinforcement Learning Methodology to Predict the Progression of Colorectal Cancer

DescriptionIn cancer biology, large amounts of high dimensional data (genomic, transcriptomic, proteomic, phenotypic, etc.) are required for any computationally relevant work. The problem is further complicated by the sheer size of the human genome, roughly three billion base pairs long. Therefore, computation is time-consuming and data-intensive. To solve this problem for human colorectal cancer, we are implementing a machine learning engine based on inverse reinforcement learning, and includes several different kinds of neural networks to perform data preparation, training, and prediction. Our work aims to reconstruct the progression of tumor development in a sample, and predict the next steps of its evolution, to aid in diagnosis and treatment. This poster will be presented as a work in progress methodology.

Authors

Silba Dowell

University of Wyoming

Daniel Hintz

University of Wyoming

Tyson Limato

University of Wyoming

Shad Sellers

University of Wyoming

Milana Wolff

University of Wyoming

Nicholas Chia