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。
MS2MetGAN: Latent-space adversarial training for metabolite–spectrum matching in MS/MS database search
University of Tennessee at Chattanooga | Middle Georgia State University
30秒速读
IN SHORT: This paper addresses the critical bottleneck in metabolite identification: the generation of high-quality negative training samples that are structurally similar to true metabolites, which is essential for training robust machine learning classifiers.
核心创新
- Methodology Introduces a novel latent-space approach where metabolite structures and MS/MS spectra are encoded into numerical vectors using autoencoders, transforming metabolite-spectrum matching into vector matching.
- Methodology Proposes a GAN framework specifically designed to generate challenging decoy metabolite latent vectors conditioned on spectrum latent vectors, creating more informative negative training samples.
- Methodology Demonstrates that adversarial training (GAN-9) significantly improves classifier stability, reducing standard deviation of accuracy across datasets from 0.3286 (GAN-0) to 0.1618 while increasing mean accuracy.
主要结论
- MS2MetGAN achieves superior overall performance with mean accuracy of 76.33% against MetaCyc database and 79.35% against isomer decoys, outperforming 8 baseline tools including MIDAS (69.21%), SF-Matching (65.79%), and CSI:FingerID (49.66%).
- The GAN training procedure improves performance stability across diverse test datasets, reducing standard deviation of accuracy from 0.3286 (GAN-0) to 0.1618 (GAN-9) for MetaCyc searches and from 0.3122 to 0.1663 for isomer decoy searches.
- MS2MetGAN demonstrates strong transferability, outperforming baseline tools on 66.67%-100% of test datasets in pairwise comparisons, with particularly strong performance against isomer decoys where it beats all baselines on 77.78%-100% of datasets.
摘要: Database search is a widely used approach for identifying metabolites from tandem mass spectra (MS/MS). In this strategy, an experimental spectrum is matched against a user-specified database of candidate metabolites, and candidates are ranked such that true metabolite–spectrum matches receive the highest scores. Machine-learning methods have been widely incorporated into database-search–based identification tools and have substantially improved performance. To further improve identification accuracy, we propose a new framework for generating negative training samples. The framework first uses autoencoders to learn latent representations of metabolite structures and MS/MS spectra, thereby recasting metabolite–spectrum matching as matching between latent vectors. It then uses a GAN to generate latent vectors of decoy metabolites and constructs decoy metabolite–spectrum matches as negative samples for training. Experimental results show that our tool, MS2MetGAN, achieves better overall performance than existing metabolite identification methods.