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...
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.