Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
Bayesian Inference in Epidemic Modelling: A Beginner’s Guide Illustrated with the SIR Model
PhD Mathematics
30秒速读
IN SHORT: This guide addresses the core challenge of estimating uncertain epidemiological parameters (like transmission and recovery rates) from noisy, real-world outbreak data by providing a clear, applied pathway using Bayesian inference and MCMC.
核心创新
- Methodology Presents an integrated, pedagogical pipeline from the SIR ODE model through Bayesian likelihood formulation to practical MCMC implementation, demystifying the process for beginners.
- Methodology Explicitly connects the Gaussian noise assumption in the likelihood to the common least-squares fitting approach, framing Bayesian inference as its natural probabilistic extension with uncertainty quantification.
- Theory Emphasizes the interpretative power of the full posterior distribution and credible intervals over single point estimates, highlighting this as the key advantage for decision-making under uncertainty.
主要结论
- In a synthetic example with true parameters β=0.3, γ=0.1 (R0=3.0), MCMC recovered posterior means of β=0.300 (std 0.002) and γ=0.102 (std 0.001), demonstrating accurate and precise inference.
- The posterior distribution for R0 was estimated as 2.95 with a standard deviation of 0.03, showing the method successfully quantifies uncertainty in this critical epidemiological metric.
- The framework successfully separates the roles of individual parameters β and γ, showing that different pairs can yield the same R0 but produce distinct epidemic curve shapes (e.g., peak sharpness), which point estimates alone would miss.
摘要: This guide provides a beginner-friendly introduction to Bayesian inference in the context of epidemic modeling, using the classic Susceptible-Infected-Recovered (SIR) model as a working example. It covers the mathematical setup of the SIR ordinary differential equations, the formulation of the Bayesian inference problem (likelihood and prior specification), and the implementation of Markov Chain Monte Carlo (MCMC) via the Metropolis-Hastings algorithm to estimate transmission (β) and recovery (γ) rates from noisy outbreak data. The tutorial emphasizes the conceptual advantages of the Bayesian framework—which provides full posterior distributions quantifying parameter uncertainty—over frequentist point estimates, and walks through a complete synthetic example with results and interpretation.