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| 2. |
- Jiang, Richard, et al.
(författare)
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Epidemiological modeling in StochSS Live!
- 2021
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:17, s. 2787-2788
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Tidskriftsartikel (refereegranskat)abstract
- We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results.StochSS Live! is freely available at https://live.stochss.org/Supplementary data are available at Bioinformatics online.
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| 3. |
- Jiang, Richard, et al.
(författare)
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Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods
- 2022
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 18:1
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Tidskriftsartikel (refereegranskat)abstract
- Identifying the reactions that govern a dynamical biological system is a crucial but challenging task in systems biology. In this work, we present a data-driven method to infer the underlying biochemical reaction system governing a set of observed species concentrations over time. We formulate the problem as a regression over a large, but limited, mass-action constrained reaction space and utilize sparse Bayesian inference via the regularized horseshoe prior to produce robust, interpretable biochemical reaction networks, along with uncertainty estimates of parameters. The resulting systems of chemical reactions and posteriors inform the biologist of potentially several reaction systems that can be further investigated. We demonstrate the method on two examples of recovering the dynamics of an unknown reaction system, to illustrate the benefits of improved accuracy and information obtained.
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| 4. |
- Jiang, Richard M., et al.
(författare)
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Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators
- 2021
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Ingår i: BMC Bioinformatics. - : BioMed Central (BMC). - 1471-2105. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- Background: Approximate Bayesian Computation (ABC) has become a key tool for calibrating the parameters of discrete stochastic biochemical models. For higher dimensional models and data, its performance is strongly dependent on having a representative set of summary statistics. While regression-based methods have been demonstrated to allow for the automatic construction of effective summary statistics, their reliance on first simulating a large training set creates a significant overhead when applying these methods to discrete stochastic models for which simulation is relatively expensive. In this tau work, we present a method to reduce this computational burden by leveraging approximate simulators of these systems, such as ordinary differential equations and tau-Leaping approximations.Results: We have developed an algorithm to accelerate the construction of regression-based summary statistics for Approximate Bayesian Computation by selectively using the faster approximate algorithms for simulations. By posing the problem as one of ratio estimation, we use state-of-the-art methods in machine learning to show that, in many cases, our algorithm can significantly reduce the number of simulations from the full resolution model at a minimal cost to accuracy and little additional tuning from the user. We demonstrate the usefulness and robustness of our method with four different experiments.Conclusions: We provide a novel algorithm for accelerating the construction of summary statistics for stochastic biochemical systems. Compared to the standard practice of exclusively training from exact simulator samples, our method is able to dramatically reduce the number of required calls to the stochastic simulator at a minimal loss in accuracy. This can immediately be implemented to increase the overall speed of the ABC workflow for estimating parameters in complex systems.
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| 5. |
- Singh, Prashant, et al.
(författare)
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Scalable machine learning-assisted model exploration and inference using Sciope
- 2021
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:2, s. 279-281
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Tidskriftsartikel (refereegranskat)abstract
- Discrete stochastic models of gene regulatory networks are fundamental tools for in silico study of stochastic gene regulatory networks. Likelihood-free inference and model exploration are critical applications to study a system using such models. However, the massive computational cost of complex, high-dimensional and stochastic modelling currently limits systematic investigation to relatively simple systems. Recently, machine-learning-assisted methods have shown great promise to handle larger, more complex models. To support both ease-of-use of this new class of methods, as well as their further development, we have developed the scalable inference, optimization and parameter exploration (Sciope) toolbox. Sciope is designed to support new algorithms for machine-learning-assisted model exploration and likelihood-free inference. Moreover, it is built ground up to easily leverage distributed and heterogeneous computational resources for convenient parallelism across platforms from workstations to clouds.The Sciope Python3 toolbox is freely available on https://github.com/Sciope/Sciope, and has been tested on Linux, Windows and macOS platforms.Supplementary information is available at Bioinformatics online.
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| 6. |
- Wrede, Fredrik
(författare)
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Large-scale simulation-based experiments with stochastic models using machine learning-assisted approaches : Applications in systems biology using Markov jump processes
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Discrete and stochastic models in systems biology, such as biochemical reaction networks, can be modeled as Markov jump processes. The chemical master equation describes how the probability distribution of a biochemical system's states evolves. Unfortunately, solutions to the chemical master equation only exist for trivial problems. However, the stochastic simulation algorithm (SSA) can generate exact sample paths. Large-scale simulation-based experiments involving variations to the model's parameters are computationally intensive and hinder modelers from exploring and inferring their models due to high-dimensional models.This thesis proposes methodologies and tools for model exploration and approximate parameter inference of high-dimensional stochastic models simulated via the SSA. We propose a smart computational workflow using machine learning-assisted approaches to enable model exploration of gene regulatory networks where the objective is to assess different qualitative behaviors present in the model. An artificial neural network is proposed for learning summary statistics used in approximate parameter inference. The neural network can find distinct local features from multivariate time series, enabling more complex models involving several biological species. By introducing epistemic uncertainty, we further explore Bayesian neural networks for approximate parameter inference. A classification approach is introduced, which learns the proposal posterior by an adaptive sampling scheme, ultimately reducing the number of simulations required for the inference task. We have also developed the software package Sciope to support modelers with machine learning-assisted techniques for model exploration and parameter inference. Sciope also comes with various features, such as experimental designs, traditional ABC algorithms, and a parallel backend to scale large simulation-based experiments from laptops to the cloud.Finally, to reduce the gap between modelers and biologists, StochSS Live! has been developed. StochSS Live! is a user-friendly web-based platform that enables any practitioners to build biochemical reaction models and perform simulation by ensemble analysis, model exploration, and approximate parameter inference.
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| 7. |
- Wrede, Fredrik, et al.
(författare)
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Robust and integrative Bayesian neural networks for likelihood-free parameter inference
- 2022
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Ingår i: 2022 International Joint Conference on Neural Networks (IJCNN). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665495264 - 9781728186719 ; , s. 1-10
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Konferensbidrag (refereegranskat)abstract
- State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference. Existing approaches for learning summarizing networks are mainly based on deterministic neural networks, and do not take network prediction uncertainty into account. This work proposes a robust integrated approach that learns summary statistics using Bayesian neural networks, and produces a proposal posterior density using categorical distributions. An adaptive sampling scheme selects simulation locations to efficiently and iteratively refine the predictive proposal posterior of the network conditioned on observations. This allows for more efficient and robust convergence on comparatively large prior spaces. The approximated proposal posterior can then either be processed through a correction mechanism, or be used in conjunction with a density estimator to arrive at the true posterior. We demonstrate our approach on benchmark examples.
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| 8. |
- Åkesson, Mattias, et al.
(författare)
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Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation
- 2022
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Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - : IEEE. - 1545-5963 .- 1557-9964. ; 19:6, s. 3353-3365
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Tidskriftsartikel (refereegranskat)abstract
- Approximate Bayesian Computation is widely used in systems biology for inferring parameters in stochastic gene regulatory network models. Its performance hinges critically on the ability to summarize high-dimensional system responses such as time series into a few informative, low-dimensional summary statistics. The quality of those statistics acutely impacts the accuracy of the inference task. Existing methods to select the best subset out of a pool of candidate statistics do not scale well with large pools of several tens to hundreds of candidate statistics. Since high quality statistics are imperative for good performance, this becomes a serious bottleneck when performing inference on complex and high-dimensional problems.This paper proposes a convolutional neural network architecture for automatically learning informative summary statistics of temporal responses. We show that the proposed network can effectively circumvent the statistics selection problem of the preprocessing step for ABC inference. The proposed approach is demonstrated on two benchmark problem and one challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator. We also study the impact of experimental design on network performance by comparing different data richness and data acquisition strategies.
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