Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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。