Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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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 ...
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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...
Tree Thinking in the Genomic Era: Unifying Models Across Cells, Populations, and Species
Stanford University | University of Oxford | University of California, Berkeley | Peking University | Guangzhou Medical University
The 30-Second View
IN SHORT: This paper addresses the fragmentation of tree-based inference methods across biological scales by identifying shared algorithmic principles and statistical challenges in phylogenetics, population genetics, and cell lineage tracing.
Innovation (TL;DR)
- Methodology Identifies deep conceptual parallels between phylogenetic placement algorithms and ARG threading methods, demonstrating how phylogenetic placement generalizes to ARG reconstruction.
- Biology Shows that quartet-based network methods in phylogenetics and ABBA-BABA statistics in population genetics capture the same underlying signal of gene flow through asymmetric genealogical relationships.
- Methodology Demonstrates how ARG-based migration inference methods (e.g., GAIA, spacetrees) extend classical phylogeographic approaches by leveraging the full sequence of locally correlated genealogies along the genome.
Key conclusions
- Tree-based models provide a unified framework for ancestry inference across biological scales, with ARGs representing ~2.48 million SARS-CoV-2 genomes demonstrating pandemic-scale feasibility.
- Methodological parallels exist across domains: phylogenetic placement algorithms share core logic with ARG threading, and quartet-based methods in phylogenetics mirror ABBA-BABA statistics in population genetics for detecting gene flow.
- Current ARG inference algorithms remain constrained by simplifying assumptions (neutrality, panmixia, constant population size) and face challenges in uncertainty quantification, particularly for non-model species or limited sample sizes.
Abstract: The ongoing explosion of genome sequence data is transforming how we reconstruct and understand the histories of biological systems. Across biological scales–from individual cells to populations and species–trees-based models provide a common framework for representing ancestry. Once limited to species phylogenetics, “tree thinking” now extends deeply to population genomics and cell biology, revealing the genealogical structure of genetic and phenotypic variation within and across organisms. Recently, there have been great methodological and computational advances on tree-based methods, including methods for inferring ancestral recombination graphs in populations, phylogenetic frameworks for comparative genomics, and lineage-tracing techniques in developmental and cancer biology. Despite differences in data types and biological contexts, these approaches share core statistical and algorithmic challenges: efficiently inferring branching histories from genomic information, integrating temporal and spatial signals, and connecting genealogical structures to evolutionary and functional processes. Recognizing these shared foundations opens opportunities for cross-fertilization between fields that are traditionally studied in isolation. By examining how tree-based methods are applied across cellular, population, and species scales, we identify the conceptual parallels that unite them and the distinct challenges that each domain presents. These comparisons offer new perspectives that can inform algorithmic innovations and lead to more powerful inference strategies across the full spectrum of biological systems.