2026-09-19T03:47:52Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1393342022-01-15T04:08:47Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Berger, Bonnie
Katz, Boris
Yaari, Adam Uri
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-01-14T15:04:43Z
2022-01-14T15:04:43Z
2021-06
2021-06-24T19:42:23.070Z
https://hdl.handle.net/1721.1/139334
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.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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http://rightsstatements.org/page/InC-EDU/1.0/
Multi-resolution modeling of a discrete stochastic process identifies causes of cancer
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
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unknown
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<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.</Abstract>
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