Paper List
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STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
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Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
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Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
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Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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.