Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
Geometric framework for biological evolution
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30秒速读
IN SHORT: This paper addresses the fundamental challenge of developing a coordinate-independent, geometric description of evolutionary dynamics that bridges genotype and phenotype spaces, revealing evolution as a learning process.
核心创新
- Methodology Establishes a generally covariant framework for evolutionary dynamics that operates consistently across genotype and phenotype spaces, enabling coordinate-independent analysis.
- Theory Demonstrates through maximum entropy principle that the inverse metric tensor equals the covariance matrix, transforming the Lande equation into a covariant gradient ascent equation.
- Methodology Models evolution as a learning process where the specific optimization algorithm is determined by the functional relationship g(κ) between metric tensor and noise covariance.
主要结论
- The maximum entropy principle yields fundamental identification: g^{αr,βs} = c^{αr,βs} (inverse metric equals genotypic covariance matrix).
- The Lande equation transforms to covariant gradient ascent: dx̄^i/dt = G^{ij}(x̄) ∂ℱ(x̄)/∂x̄^j, where G^{ij} = C^{ij} (inverse phenotype metric equals phenotypic covariance).
- Evolution implements specific learning algorithms determined by functional relation g(κ) between metric and noise covariance, with three regimes identified: quantum (α=1), efficient learning (α=1/2), and equilibration (α=0).
摘要: We develop a generally covariant description of evolutionary dynamics that operates consistently in both genotype and phenotype spaces. We show that the maximum entropy principle yields a fundamental identification between the inverse metric tensor and the covariance matrix, revealing the Lande equation as a covariant gradient ascent equation. This demonstrates that evolution can be modeled as a learning process on the fitness landscape, with the specific learning algorithm determined by the functional relation between the metric tensor and the noise covariance arising from microscopic dynamics. While the metric (or the inverse genotypic covariance matrix) has been extensively characterized empirically, the noise covariance and its associated observable (the covariance of evolutionary changes) have never been directly measured. This poses the experimental challenge of determining the functional form relating metric to noise covariance.