Paper List
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STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
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Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
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Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
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Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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.