Research Posters Archive

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

Authors: Silba Dowell, Daniel Hintz, Tyson Limato, Shad Sellers, and Milana Wolff (University of Wyoming); Nicholas Chia (Argonne National Laboratory (ANL)); and Liudmila Mainzer (University of Wyoming)

Abstract: In 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.

Best Poster Finalist (BP): no

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