Paper List
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A Unified Variational Principle for Branching Transport Networks: Wave Impedance, Viscous Flow, and Tissue Metabolism
This paper solves the core problem of predicting the empirically observed branching exponent (α≈2.7) in mammalian arterial trees, which neither Murray...
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Household Bubbling Strategies for Epidemic Control and Social Connectivity
This paper addresses the core challenge of designing household merging (social bubble) strategies that effectively control epidemic risk while maximiz...
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Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening
This paper addresses the core challenge of bridging the gap between scalable chemical structure screening and biologically informative but resource-in...
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A mechanical bifurcation constrains the evolution of cell sheet folding in the family Volvocaceae
This paper addresses the core problem of why there is an evolutionary gap in species with intermediate cell numbers (e.g., 256 cells) in Volvocaceae, ...
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Bayesian Inference in Epidemic Modelling: A Beginner’s Guide Illustrated with the SIR Model
This guide addresses the core challenge of estimating uncertain epidemiological parameters (like transmission and recovery rates) from noisy, real-wor...
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Geometric framework for biological evolution
This paper addresses the fundamental challenge of developing a coordinate-independent, geometric description of evolutionary dynamics that bridges gen...
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A multiscale discrete-to-continuum framework for structured population models
This paper addresses the core challenge of systematically deriving uniformly valid continuum approximations from discrete structured population models...
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Whole slide and microscopy image analysis with QuPath and OMERO
使QuPath能够直接分析存储在OMERO服务器中的图像而无需下载整个数据集，克服了大规模研究的本地存储限制。
用于量子退火优化的二元潜在蛋白质适应度景观
University of Alabama at Birmingham
30秒速读
IN SHORT: 通过将序列映射到二元潜在空间进行基于QUBO的适应度优化，桥接蛋白质表示学习和组合优化。
核心创新
- Methodology First framework to transform protein language model embeddings into binary latent representations for QUBO-based fitness modeling
- Methodology Enables direct compatibility with quantum annealing hardware through native QUBO formulation
- Biology Demonstrates that simple binary representations can capture meaningful structure in protein fitness landscapes
主要结论
- Q-BioLat在ProteinGym GFP数据集（10,000个样本，潜在维度32-64）上实现了0.385-0.413的Spearman相关性
- 优化后的序列始终检索到适应度百分位顶部的最近邻，模拟退火在代理分数上实现了1.529±的改进
- 遗传算法在更高维潜在空间（m=64）中优于其他方法，而局部搜索能更好地保持序列真实性
摘要: 我们提出了Q-BioLat，一个在二元潜在空间中建模和优化蛋白质适应度景观的框架。从蛋白质序列出发，我们利用预训练的蛋白质语言模型获得连续嵌入，然后将其转换为紧凑的二元潜在表示。在这个空间中，蛋白质适应度使用二次无约束二元优化（QUBO）模型进行近似，从而通过经典启发式方法（如模拟退火和遗传算法）实现高效的组合搜索。在ProteinGym基准测试中，我们证明Q-BioLat能够捕捉蛋白质适应度景观中的有意义结构，并能够识别高适应度变体。尽管使用了简单的二值化方案，我们的方法始终能检索到其最近邻位于训练适应度分布顶部的序列，特别是在最强配置下。我们进一步表明，不同的优化策略表现出不同的行为，进化搜索在更高维的潜在空间中表现更好，而局部搜索在保持真实序列方面仍具有竞争力。除了其经验性能外，Q-BioLat为蛋白质表示学习和组合优化之间提供了自然的桥梁。通过将蛋白质适应度表述为QUBO问题，我们的框架与新兴的量子退火硬件直接兼容，为量子辅助蛋白质工程开辟了新的方向。