ArticleMolecular biology and evolution2022
From Easy to Hopeless-Predicting the Difficulty of Phylogenetic Analyses.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
25 citing papers in PubMed.
- kamino: fast proteome-wide variant calling for amino acid phylogenomics.Molecular biology and evolution · 2026Article
- A deep learning-based score to evaluate multiple sequence alignments.Molecular biology and evolution · 2026Article
- Treeline Provides a Unified Strategy for Optimising Phylogenetic Trees Under Alternative Criteria.Molecular ecology resources · 2026Article
- HPV16 genetic variation provides evidence of positive natural selection driven by HLA class I.Nature communications · 2026Article
- Phylogeny of waterfowl (Anseriformes) constructed using genome sequences provides insights into topological incongruences.Molecular biology and evolution · 2026Article
- Learning to explore tree neighbourhoods for phylogenetic inference.Briefings in bioinformatics · 2026Article
- Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to Evade (Over-)optimization.Systematic biology · 2025Article
- Pruning the Tree: Comparing OTUs and ASVs in High-Throughput Sequencing of 5S-IGS Nuclear Ribosomal DNA in Phylogenetic Studies.Ecology and evolution · 2025Article
- Phylogenetic Tree Instability After Taxon Addition: Empirical Frequency, Predictability, and Consequences For Online Inference.Systematic biology · 2025Article
- MixtureFinder: Estimating DNA Mixture Models for Phylogenetic Analyses.Molecular biology and evolution · 2025Article
- Performance assessment of phylogenetic inference tools using PhyloSmew.Bioinformatics advances · 2025Article
- Advances and challenges in understanding evolution through genome comparison: meeting report of the European Molecular Biology Organization (EMBO) lecture course "Evolutionary and Comparative Genomics".Bioinformatics advances · 2025Article
- Predicting Phylogenetic Bootstrap Values via Machine Learning.Molecular biology and evolution · 2024Article
- Comparative Genomics Uncovers the Evolutionary Dynamics of Detoxification and Insecticide Target Genes Across 11 Phlebotomine Sand Flies.Genome biology and evolution · 2024Article
- The Mitochondrial Genome ofInternational journal of molecular sciences · 2024Article
- A machine-learning-based alternative to phylogenetic bootstrap.Bioinformatics (Oxford, England) · 2024Article
- The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning.Molecular biology and evolution · 2024Article
- Article
- AleRax: a tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss.Bioinformatics (Oxford, England) · 2024Article
- Phylogenetic reconciliation: making the most of genomes to understand microbial ecology and evolution.The ISME journal · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phylogenetic analyzes under the Maximum-Likelihood (ML) model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating ML-based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyzes, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.