Multi-resolution modeling of discrete stochastic processes
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Solution Overview
Problem
Current methods for modeling non-stationary discrete stochastic processes, such as cancer mutation rates, are computationally expensive and inefficient, particularly when dealing with large datasets and non-coding regions, often leading to underestimation of variance and false-positive driver predictions.
Innovation Solution
The implementation of a Split Poisson-Gamma (SPG) distribution allows for efficient modeling of non-stationary discrete stochastic processes at multiple resolutions without retraining, using a combination of Convolutional Neural Networks and Gaussian Processes to predict rate parameters and their uncertainties, enabling accurate identification of cancer-specific driver mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-stationary kernels are used for Gaussian processes to model non-stationary processes, then modeling accuracy is improved, but computational complexity increases making it intractable on large datasets
Solution Approach 1:
The patent segments the genome into discrete bins or windows, and models mutation rates independently in each segment. This segmentation allows the use of simpler stationary models within each segment while capturing non-stationarity across segments through the overall piecewise model, avoiding the computational burden of global non-stationary kernels.
Solution Approach 2:
The patent changes parameters (mutation rates, covariate effects) at discrete boundaries between genomic segments rather than allowing continuous variation. This parameter change approach captures non-stationary behavior while maintaining computational tractability through the use of simpler local models with piecewise constant parameters.
2Reliability
If traditional methods are used to model mutation rates across the genome, then comprehensive coverage is achieved, but computational efficiency decreases and false-positive predictions increase
Solution Approach 1:
The patent replaces traditional mechanical statistical modeling approaches with a machine learning-based system that uses convolutional neural networks and other algorithms to efficiently process genomic data. This substitution enables faster computation while improving prediction accuracy through learned patterns from training data.
Solution Approach 2:
The patent performs preliminary training of models on large datasets containing mutation rates and covariates before applying them to new genomic regions. This preliminary action pre-computes important parameters and patterns, making subsequent predictions computationally efficient while maintaining high accuracy through the learned models.
3Measurement precision
If high-resolution modeling is applied to capture mutation patterns at arbitrary length scales, then identification accuracy of driver mutations is improved, but computational cost increases
Solution Approach 1:
The patent implements a dynamic modeling framework where the resolution and scope of analysis can be adjusted based on computational resources and specific research questions. The model can operate at different length scales and genomic resolutions, allowing users to balance accuracy requirements against computational constraints by selecting appropriate modeling granularity.
Data Source
AI summary
An activity of interest is modeled by a non-stationary discrete stochastic process, such as a pattern of mutations across a cancer genome. Initially, input genomic data is used to train a model to predict rate parameters and their associated uncertainty estimation for each of a set of process regions. For any arbitrary set of indexed positions of the stochastic process that are identified in an information query, the rate parameters and their associated estimation uncertainties are scaled using the model to obtain a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions. In response to a search query associated with one or more base-pairs, a result is then returned. The result, which represents deviations between the estimated and observed mutation rates, is used to identify genomic elements that have more mutations than expected and therefore constitute previously unknown driver mutations.