%0 Conference Paper %B International Conference on Learning Representations %D 2021 %T Multi-resolution modeling of a discrete stochastic process identifies causes of cancer %A Adam Uri Yaari %A Maxwell Sherman %A Oliver Clarke Priebe %A Po-Ru Loh %A Boris Katz %A Andrei Barbu %A Bonnie Berger %X

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.

%B International Conference on Learning Representations %8 09/2020 %G eng %U https://openreview.net/forum?id=KtH8W3S_RE