Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
Bayesian Intelligence | Monash University | The Kids Research Institute Australia | University of Sydney | The Children's Hospital at Westmead
The 30-Second View
IN SHORT: This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowledge to guide clinical trial design and enable robust causal inference.
Innovation (TL;DR)
- Methodology Developed a comprehensive Bayesian causal model (DAG/BN) integrating 4 domains (background factors, treatments, exacerbation episode, outcomes) with 30+ nodes representing key pathophysiological processes
- Methodology Implemented a structured expert elicitation process involving 30+ CF clinicians across multiple workshops (2017-2019) using Delphi/nominal group techniques for variable selection and validation
- Biology Explicitly models the causal pathways between abnormal mucus clearance, pathogen colonization (Pseudomonas aeruginosa, MRSA, etc.), infection, and inflammation - enabling targeted treatment effect analysis
Key conclusions
- The BEAT-CF causal model successfully integrates expert knowledge from 30+ clinicians into a formal DAG structure with 4 domains and 30+ nodes, validated through multiple workshops (2017-2019)
- The framework enables explicit causal inference by identifying necessary adjustments for statistical analyses, directly guiding data collection design for clinical trials
- The model provides a reusable, transparent framework that captures key relationships between background factors (lung disease age, CFTR mutations), treatments (antibiotics, anti-inflammatories), and outcomes (lung function decline, mortality)
Abstract: Loss of lung function in cystic fibrosis (CF) occurs progressively, punctuated by acute pulmonary exacerbations (PEx) in which abrupt declines in lung function are not fully recovered. A key component of CF management over the past half century has been the treatment of PEx to slow lung function decline. This has been credited with improvements in survival for people with CF (PwCF), but there is no consensus on the optimal approach to PEx management. BEAT-CF (Bayesian evidence-adaptive treatment of CF) was established to build an evidence-informed knowledge base for CF management. The BEAT-CF causal model is a directed acyclic graph (DAG) and Bayesian network (BN) for PEx that aims to inform the design and analysis of clinical trials comparing the effectiveness of alternative approaches to PEx management. The causal model describes relationships between background risk factors, treatments, and pathogen colonisation of the airways that affect the outcome of an individual PEx episode. The key factors, outcomes, and causal relationships were elicited from CF clinical experts and together represent current expert understanding of the pathophysiology of a PEx episode, guiding the design of data collection and studies and enabling causal inference. Here, we present the DAG that documents this understanding, along with the processes used in its development, providing transparency around our trial design and study processes, as well as a reusable framework for others.