Multi-resolution modeling of a discrete stochastic process identifies causes of cancer
Name
Yaari-yaari-SM-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
2.54 MB
Format
Adobe PDF
Checksum (MD5)
d66a48badd4f1b5426100770322f39a8
Author(s)
Yaari, Adam Uri
Advisor(s)
Berger, Bonnie
Katz, Boris
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
Detection of cancer-causing mutations within the vast and mostly unexplored human genome is a major challenge. Doing so requires modeling the background mutation rate, a highly non-stationary stochastic process, across regions of interest varying in size from one to millions of positions. Here, we present the split-Poisson-Gamma (SPG) distribution, an extension of the classical Poisson-Gamma formulation, to model a discrete stochastic process at multiple resolutions. We demonstrate that the probability model has a closed-form posterior, enabling efficient and accurate linear-time prediction over any length scale after the parameters of the model have been inferred a single time. We apply our framework to model mutation rates in tumors and show that model parameters can be accurately inferred from high-dimensional epigenetic data using a convolutional neural network, Gaussian process, and maximum-likelihood estimation. Our method is both more accurate and more efficient than existing models over a large range of length scales. We demonstrate the usefulness of multi-resolution modeling by detecting genomic elements that drive tumor emergence and are of vastly differing sizes.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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