Paper List
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A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
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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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Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
This paper addresses the critical bottleneck in therapeutic peptide design: how to efficiently optimize lead peptides with geometric constraints while...
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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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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
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DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
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Cross-Species Antimicrobial Resistance Prediction from Genomic Foundation Models
This paper addresses the core challenge of predicting antimicrobial resistance across phylogenetically distinct bacterial species, where traditional m...
癌症-细菌疗法的数学建模：基于物理信息神经网络的数值模拟与分析
International University of Rabat | Université de Lorraine, CNRS, CRAN | Université de Lorraine, CNRS, IECL | Univ Rennes, INSA, CNRS, IRMAR-UMR 6625
30秒速读
IN SHORT: 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。
核心创新
- Methodology First coupled PDE model integrating tumor, bacteria, oxygen, immunosuppressive cytokines, and quorum-sensing signals for bacterial cancer therapy.
- Theory Proved global well-posedness and stability of the five-species reaction-diffusion system, establishing a rigorous mathematical foundation.
- Methodology Established convergence guarantees for PINNs on this nonlinear system, with an overall error bound of 𝒪(n^{-2}ln^{4}(n)+N^{-1/2}).
主要结论
- PINN框架实现了𝒪(n^{-2}ln^{4}(n)+N^{-1/2})的可量化误差率，能够对耦合系统进行准确的无网格模拟。
- 敏感性分析表明，治疗效果高度依赖于缺氧维持（通过KH/(KH+O)建模）和细菌的氧耐受性。
- 该模型识别了三个生物学相关的稳态，并确认扩散不会引发图灵不稳定性，表明时空动力学的稳定性。
摘要: 细菌癌症疗法利用厌氧细菌靶向缺氧肿瘤区域的能力，然而肿瘤生长、细菌定植、氧气水平、免疫抑制细胞因子和细菌通讯之间的相互作用仍然缺乏量化。我们提出了一个二维组织域中五个耦合非线性反应-扩散方程的数学模型。我们证明了模型的全局适定性，并确定了其稳态以分析稳定性。此外，物理信息神经网络（PINN）无需网格和大量数据即可求解该系统。它通过结合残差稳定性和Sobolev近似误差界提供了收敛保证。这导致整体误差率为𝒪(n^{-2}ln^{4}(n)+N^{-1/2})，该误差率取决于网络宽度n和配置点数量N。我们进行了多项数值实验，包括预测肿瘤对治疗的反应。我们还对某些参数进行了敏感性分析。结果表明，长期治疗效果可能需要维持肿瘤中的缺氧区域，或者使用更能耐受氧气的细菌，这对于持久的肿瘤控制可能是必要的。