Paper List
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Evolutionarily Stable Stackelberg Equilibrium
通过要求追随者策略对突变入侵具有鲁棒性，弥合了斯塔克尔伯格领导力模型与演化稳定性之间的鸿沟。
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Recovering Sparse Neural Connectivity from Partial Measurements: A Covariance-Based Approach with Granger-Causality Refinement
通过跨多个实验会话累积协方差统计，实现从部分记录到完整神经连接性的重建。
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Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
ATMOS通过提供一个基于SSM的高效框架，用于生物分子的原子级轨迹生成，弥合了计算昂贵的MD模拟与时间受限的深度生成模型之间的差距。
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Slow evolution towards generalism in a model of variable dietary range
通过证明是种群统计噪声（而非确定性动力学）驱动了模式形成和泛化食性的演化，解决了间接竞争下物种形成的悖论。
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Grounded Multimodal Retrieval-Augmented Drafting of Radiology Impressions Using Case-Based Similarity Search
通过将印象草稿基于检索到的历史病例，并采用明确引用和基于置信度的拒绝机制，解决放射学报告生成中的幻觉问题。
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Unified Policy–Value Decomposition for Rapid Adaptation
通过双线性分解在策略和价值函数之间共享低维目标嵌入，实现对新颖任务的零样本适应。
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Mathematical Modeling of Cancer–Bacterial Therapy: Analysis and Numerical Simulation via Physics-Informed Neural Networks
提供了一个严格的、无网格的PINN框架，用于模拟和分析细菌癌症疗法中复杂的、空间异质的相互作用。
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Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift
通过从无标记分子谱中学习可迁移表征，利用最少的临床数据实现患者药物反应的有效预测。
A Standardized Framework for Evaluating Gene Expression Generative Models
University of Cambridge | Wellcome Sanger Institute | Sapienza University of Rome | ISTI-CNR
30秒速读
IN SHORT: This paper addresses the critical lack of standardized evaluation protocols for single-cell gene expression generative models, where inconsistent metric implementations and computation spaces make cross-study comparisons impossible.
核心创新
- Methodology Introduces GGE, the first unified Python framework with explicit computation space parameterization (raw, PCA, DEG-restricted) for standardized evaluation of generative models.
- Methodology Proposes perturbation-effect correlation metric that measures direction and magnitude of perturbation responses rather than raw expression correlation, focusing evaluation on biologically relevant signals.
- Methodology Demonstrates that Wasserstein distance values vary by nearly an order of magnitude (17.2 to 104.3) depending solely on computation space, quantifying the standardization problem.
主要结论
- Metric values vary substantially with implementation choices: W₂ distance ranges from 17.2 (PCA-25) to 104.3 (raw space) on identical data, highlighting critical need for standardization.
- DEG selection strategy affects correlation metrics: top-20 DEG selection yields Pearson correlation of 0.614±0.066 vs strict threshold selection (lfc>1, p<0.01) yielding 0.506±0.217 on Norman dataset.
- Perturbation-effect correlation in DEG space provides biologically meaningful evaluation, focusing on genes that actually respond to perturbations rather than steady-state background expression.
摘要: The rapid development of generative models for single-cell gene expression data has created an urgent need for standardised evaluation frameworks. Current evaluation practices suffer from inconsistent metric implementations, incomparable hyperparameter choices, and a lack of biologically-grounded metrics. We present Generated Genetic Expression Evaluator (GGE), an open-source Python framework that addresses these challenges by providing a comprehensive suite of distributional metrics with explicit computation space options and biologically-motivated evaluation through differentially expressed gene (DEG)-focused analysis and perturbation-effect correlation, enabling standardized reporting and reproducible benchmarking. Through extensive analysis of the single-cell generative modeling literature, we identify that no standardized evaluation protocol exists. Methods report incomparable metrics computed in different spaces with different hyperparameters. We demonstrate that metric values vary substantially depending on implementation choices, highlighting the critical need for standardization. GGE enables fair comparison across generative approaches and accelerates progress in perturbation response prediction, cellular identity modeling, and counterfactual inference.