ArticleProceedings. Euromicro International Conference on Parallel, Distributed, and Network-based Processing2018
Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology.
Article in Proceedings. Euromicro International Conference on Parallel, Distributed, and Network-based Processing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.Cell systems · 2024Article
- Monte Carlo samplers for efficient network inference.PLoS computational biology · 2023Article
- Bayesian parameter estimation for dynamical models in systems biology.PLoS computational biology · 2022Article
- Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit.Bioinformatics (Oxford, England) · 2022Article
- Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry.Computational intelligence and neuroscience · 2022Article
- Inhibition of RPS6K reveals context-dependent Akt activity in luminal breast cancer cells.PLoS computational biology · 2021Article
- Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage.PLoS computational biology · 2021Article
- A differential evolution-based regression framework for forecasting Bitcoin price.Annals of operations research · 2021Article
- Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach.Neural computing & applications · 2020Review
- Parallel Tempering with Lasso for model reduction in systems biology.PLoS computational biology · 2020Article
- A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models.Royal Society open science · 2020Article
- Short-term stock market price trend prediction using a comprehensive deep learning system.Journal of big data · 2020Article
- A physiologically-motivated model of cystic fibrosis liquid and solute transport dynamics across primary human nasal epithelia.Journal of pharmacokinetics and pharmacodynamics · 2019Article
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- Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning.Entropy (Basel, Switzerland) · 2019Article
- Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems.Entropy (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://github.com/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.