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In this paper, we propose DeepTree, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them. We call our deep neural model situated latent because its behavior is…

Machine Learning · Computer Science 2023-05-10 Xiaochen Zhou , Bosheng Li , Bedrich Benes , Songlin Fei , Sören Pirk

Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms. This hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper,…

Machine Learning · Statistics 2024-05-24 Cheng Zhang , Frederick A. Matsen

Decision trees are a commonly used class of machine learning models valued for their interpretability and versatility, capable of both classification and regression. We propose ZTree, a novel decision tree learning framework that replaces…

Machine Learning · Computer Science 2025-09-17 Eric Cheng , Jie Cheng

Compatibility of phylogenetic trees is the most important concept underlying widely-used methods for assessing the agreement of different phylogenetic trees with overlapping taxa and combining them into common supertrees to reveal the tree…

Discrete Mathematics · Computer Science 2011-11-09 Merce Llabres , Jairo Rocha , Francesc Rossello , Gabriel Valiente

Tree structures appear in many fields of the life sciences, including phylogenetics, developmental biology and nucleic acid structures. Trees can be used to represent RNA secondary structures, which directly relate to the function of…

Machine Learning · Computer Science 2026-01-22 Pengyu Liu , Mariel Vázquez , Nataša Jonoska

Phylogenetic tree shapes capture fundamental signatures of evolution. We consider ``ranked'' tree shapes, which are equipped with a total order on the internal nodes compatible with the tree graph. Recent work has established an elegant…

Populations and Evolution · Quantitative Biology 2026-03-10 Chris Jennings-Shaffer , Ziyue , Chen , Julia A Palacios , Frederick A Matsen

Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogenies from the vast space of possible trees remains…

Populations and Evolution · Quantitative Biology 2024-01-24 Matthew J Penn , Neil Scheidwasser , Joseph Penn , Christl A Donnelly , David A Duchêne , Samir Bhatt

Phylogenetic trees summarize evolutionary relationships between organisms, and tools to analyze collections of phylogenetic trees enable contrasts between different genes' ancestry. The BHV metric space has enabled the analysis of…

Quantitative Methods · Quantitative Biology 2026-04-24 Maria Alejandra Valdez Cabrera , Amy D Willis

How a single fertilized cell gives rise to a complex array of specialized cell types in development is a central question in biology. The cells grow, divide, and acquire differentiated characteristics through poorly understood molecular…

Machine Learning · Computer Science 2025-03-26 Da Kuang , Guanwen Qiu , Junhyong Kim

The multispecies coalescent process models the genealogical relationships of genes sampled from several species, enabling useful predictions about phenomena such as the discordance between the gene tree and the species phylogeny due to…

Populations and Evolution · Quantitative Biology 2020-12-11 Jakub Truszkowski , Celine Scornavacca , Fabio Pardi

The search for similarity and dissimilarity measures on phylogenetic trees has been motivated by the computation of consensus trees, the search by similarity in phylogenetic databases, and the assessment of clustering results in…

Populations and Evolution · Quantitative Biology 2011-11-09 Francesc Rossello , Gabriel Valiente

In image classification, Convolutional Neural Network(CNN) models have achieved high performance with the rapid development in deep learning. However, some categories in the image datasets are more difficult to distinguished than others.…

Computer Vision and Pattern Recognition · Computer Science 2019-06-05 Yuntao Liu , Yong Dou , Ruochun Jin , Peng Qiao

We analyse a maximum-likelihood approach for combining phylogenetic trees into a larger `supertree'. This is based on a simple exponential model of phylogenetic error, which ensures that ML supertrees have a simple combinatorial description…

Populations and Evolution · Quantitative Biology 2007-08-17 Mike Steel , Allen Rodrigo

Evolution is a process that is influenced by various environmental factors, e.g. the interactions between different species, genes, and biogeographical properties. Hence, it is interesting to study the combined evolutionary history of…

Quantitative Methods · Quantitative Biology 2013-08-02 Nicolas Wieseke , Matthias Bernt , Martin Middendorf

Pairwise ordered tree alignment are combinatorial objects that appear in RNA secondary structure comparison. However, the usual representation of tree alignments as supertrees is ambiguous, i.e. two distinct supertrees may induce identical…

Quantitative Methods · Quantitative Biology 2016-03-08 Cedric Chauve , Julien Courtiel , Yann Ponty

A decision tree is commonly restricted to use a single hyperplane to split the covariate space at each of its internal nodes. It often requires a large number of nodes to achieve high accuracy, hurting its interpretability. In this paper,…

Machine Learning · Computer Science 2020-10-23 Mohammadreza Armandpour , Mingyuan Zhou

A method for creating a forest of model trees to fit samples of a function defined on images is described in several steps: down-sampling the images, determining a tree's hyperplanes, applying convolutions to the hyperplanes to handle small…

Machine Learning · Computer Science 2026-01-28 William Ward Armstrong , Hongyi Li , Jun Xu

RNA design aims to identify RNA sequences that fold into a target secondary structure. This task is challenging in terms of computational efficiency. Most existing methods focus on either minimum free energy (MFE)-based or ensemble-based…

Biomolecules · Quantitative Biology 2026-03-04 Tianshuo Zhou , David H. Mathews , Liang Huang

While Vision-Language Models (VLMs) excel at visual understanding, they often fail to grasp hierarchical knowledge. This leads to common errors where VLMs misclassify coarser taxonomic levels even when correctly identifying the most…

Computer Vision and Pattern Recognition · Computer Science 2026-01-22 Zhenghong Li , Kecheng Zheng , Haibin Ling

Phylogenetic trees are used to model evolution: leaves are labelled to represent contemporary species ("taxa") and interior vertices represent extinct ancestors. Informally, convex characters are measurements on the contemporary species in…

Combinatorics · Mathematics 2021-11-25 Steven Kelk , Ruben Meuwese , Stephan Wagner
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