A Comprehensive Guide to Simulation-based Inference in Computational Biology

• URL: http://arxiv.org/abs/2409.19675v1
• Date: Sun, 29 Sep 2024 12:04:03 GMT
• Title: A Comprehensive Guide to Simulation-based Inference in Computational Biology
• Authors: Xiaoyu Wang, Ryan P. Kelly, Adrianne L. Jenner, David J. Warne, Christopher Drovandi,
• Abstract summary: This paper provides comprehensive guidelines for deciding between SBI approaches for complex biological models. We apply the guidelines to two agent-based models that describe cellular dynamics using real-world data. Our study unveils a critical insight: while neural SBI methods demand significantly fewer simulations for inference results, they tend to yield biased estimations.
• Score: 5.333122501732079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational models are invaluable in capturing the complexities of real-world biological processes. Yet, the selection of appropriate algorithms for inference tasks, especially when dealing with real-world observational data, remains a challenging and underexplored area. This gap has spurred the development of various parameter estimation algorithms, particularly within the realm of Simulation-Based Inference (SBI), such as neural and statistical SBI methods. Limited research exists on how to make informed choices on SBI methods when faced with real-world data, which often results in some form of model misspecification. In this paper, we provide comprehensive guidelines for deciding between SBI approaches for complex biological models. We apply the guidelines to two agent-based models that describe cellular dynamics using real-world data. Our study unveils a critical insight: while neural SBI methods demand significantly fewer simulations for inference results, they tend to yield biased estimations, a trend persistent even with robust variants of these algorithms. On the other hand, the accuracy of statistical SBI methods enhances substantially as the number of simulations increases. This finding suggests that, given a sufficient computational budget, statistical SBI can surpass neural SBI in performance. Our results not only shed light on the efficacy of different SBI methodologies in real-world scenarios but also suggest potential avenues for enhancing neural SBI approaches. This study is poised to be a useful resource for computational biologists navigating the intricate landscape of SBI in biological modeling.

Related papers

• Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations [4.368891765870579]
  We investigate the potential of incorporating active learning into deep neural networks (DNNs) surrogate training. This allows intelligent and objective selection of training simulations, reducing the need to generate extensive simulation data. The results set the groundwork for developing the high-performance computing infrastructure for Smart Surrogates.
  arXiv  Detail & Related papers  (2024-07-10T14:00:20Z)
• Preconditioned Neural Posterior Estimation for Likelihood-free Inference [5.651060979874024]
  We show in this paper that the neural posterior estimator (NPE) methods are not guaranteed to be highly accurate, even on problems with low dimension. We propose preconditioned NPE and its sequential version (PSNPE), which uses a short run of ABC to effectively eliminate regions of parameter space that produce large discrepancy between simulations and data. We present comprehensive empirical evidence that this melding of neural and statistical SBI methods improves performance over a range of examples.
  arXiv  Detail & Related papers  (2024-04-21T07:05:38Z)
• Simulation-based Inference for Cardiovascular Models [57.92535897767929]
  We use simulation-based inference to solve the inverse problem of mapping waveforms back to plausible physiological parameters. We perform an in-silico uncertainty analysis of five biomarkers of clinical interest. We study the gap between in-vivo and in-silico with the MIMIC-III waveform database.
  arXiv  Detail & Related papers  (2023-07-26T02:34:57Z)
• Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference [0.0]
  We establish a crucial connection between ratio-based SBI inference algorithms and gradient-based variational inference by leveraging mutual information bounds. This connection allows us to extend the simultaneous optimization of experimental designs and amortized inference functions.
  arXiv  Detail & Related papers  (2023-06-27T18:15:41Z)
• Validation Diagnostics for SBI algorithms based on Normalizing Flows [55.41644538483948]
  This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF. It also offers theoretical guarantees based on results of local consistency. This work should help the design of better specified models or drive the development of novel SBI-algorithms.
  arXiv  Detail & Related papers  (2022-11-17T15:48:06Z)
• Investigating the Impact of Model Misspecification in Neural Simulation-based Inference [1.933681537640272]
  We study the behaviour of neural SBI algorithms in the presence of various forms of model misspecification. We find that misspecification can have a profoundly deleterious effect on performance. We conclude that new approaches are required to address model misspecification if neural SBI algorithms are to be relied upon to derive accurate conclusions.
  arXiv  Detail & Related papers  (2022-09-05T09:08:16Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Neural Posterior Estimation with Differentiable Simulators [58.720142291102135]
  We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
  arXiv  Detail & Related papers  (2022-07-12T16:08:04Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• SBI -- A toolkit for simulation-based inference [0.0]
  Simulation-based inference ( SBI) seeks to identify parameter sets that a) are compatible with prior knowledge and b) match empirical observations. We present $textttsbi$, a PyTorch-based package that implements SBI algorithms based on neural networks.
  arXiv  Detail & Related papers  (2020-07-17T16:53:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.