Paper List
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Translating Measures onto Mechanisms: The Cognitive Relevance of Higher-Order Information
This review addresses the core challenge of translating abstract higher-order information theory metrics (e.g., synergy, redundancy) into defensible, ...
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Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
This paper addresses the critical gap in understanding whether LLMs spontaneously develop human-like Bayesian strategies for processing uncertain info...
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Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
This work addresses the critical lack of experimentally validated channel models for molecular communication within complex vessel networks, which is ...
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Modulation of DNA rheology by a transcription factor that forms aging microgels
This work addresses the fundamental question of how the transcription factor NANOG, essential for embryonic stem cell pluripotency, physically regulat...
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Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks
This paper addresses the core challenge of distinguishing genuine stochastic dynamics of gene regulatory networks from artifacts introduced by imperfe...
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PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
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Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
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Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance
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IN SHORT: This paper addresses the critical challenge of quantitatively evaluating antibiotic prescribing policies under realistic uncertainty and partial observability, where traditional observational studies are limited by incomplete data and unmeasured confounding factors.
核心创新
- Methodology Introduces a novel 'leaky-balloon' abstraction for modeling antibiotic resistance dynamics, providing a computationally efficient yet biologically plausible representation of resistance accumulation and decay.
- Methodology Implements a modular MDP/POMDP framework with explicit control over observability parameters (noise, bias, delay), enabling systematic study of how information degradation affects optimal prescribing strategies.
- Methodology Provides the first Gymnasium-compatible simulation environment specifically designed for antibiotic stewardship research, bridging computational epidemiology and reinforcement learning communities.
主要结论
- The abx_amr_simulator provides a quantitative framework for evaluating antibiotic prescribing policies, addressing the limitation that observational studies alone cannot directly quantify long-term effects of prescribing interventions.
- The simulator's modular design enables researchers to systematically investigate how specific data deficiencies (noise, bias, delay) impede antibiotic stewardship efforts and assess potential gains from targeted interventions.
- By balancing individual clinical outcomes (λ=0) and community resistance management (λ=1) through configurable reward functions, the framework allows exploration of trade-offs between short-term patient care and long-term public health objectives.
摘要: Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To address this challenge, we introduce abx_amr_simulator, a Python-based simulation package designed to model antibiotic prescribing and AMR dynamics within a controlled, reinforcement learning (RL)-compatible environment. The simulator allows users to specify patient populations, antibiotic-specific AMR response curves, and reward functions that balance immediate clinical benefit against long-term resistance management. Key features include a modular design for configuring patient attributes, antibiotic resistance dynamics modeled via a leaky-balloon abstraction, and tools to explore partial observability through noise, bias, and delay in observations. The package is compatible with the Gymnasium RL API, enabling users to train and test RL agents under diverse clinical scenarios. From an ML perspective, the package provides a configurable benchmark environment for sequential decision-making under uncertainty, including partial observability induced by noisy, biased, and delayed observations. By providing a customizable and extensible framework, abx_amr_simulator offers a valuable tool for studying AMR dynamics and optimizing antibiotic stewardship strategies under realistic uncertainty.