ArticleMolecular phylogenetics and evolution2019
A demonstration of unsupervised machine learning in species delimitation.
Article in Molecular phylogenetics and evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 118 citations in OpenAlex.
- Genomic and Phenotypic Delimitation of Species in a Temperate Aquatic Biodiversity Hotspot.Systematic biology · 2026Article
- Genomic evidence reveals taxonomic oversplitting in a wing dimorphic grasshopper complex Tetrix japonica.BMC genomics · 2026Article
- Integrating Genomics, Collections, and Community Science to Delimit Species Clarifies the Taxonomy of a Variable Monitor Lizard (Varanus tristis).Systematic biology · 2026Article
- A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data.Computational and structural biotechnology journal · 2026Article
- Covering All Bases: A Universal Metazoan UCE Probe Set to Democratize Phylogenomics.Genome biology and evolution · 2025Article
- Speciation Pattern and Process in the California Coastal Dune Endemic Trapdoor SpiderEcology and evolution · 2025Article
- Deforestation-induced Hybridization in Philippine Frogs Creates a Distinct Phenotype With an Inviable Genotype.Heredity · 2025Article
- Phylogenomics of the rarest animals: a second species of Micrognathozoa identified by machine learning.Proceedings. Biological sciences · 2025Article
- Understanding species limits through the formation of phylogeographic lineages.Ecology and evolution · 2024Article
- Article
- Article
- A new long-read mitochondrial-genome protocol (PacBio HiFi) for haemosporidian parasites: a tool for population and biodiversity studies.Malaria journal · 2024Article
- Microgeographic population structuring in a genus of California trapdoor spiders and discovery of an enigmatic new species (Euctenizidae:Ecology and evolution · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
- Unsupervised Deep Learning Can Identify Protein Functional Groups from Unaligned Sequences.Genome biology and evolution · 2023Article
- Phylogeography and cohesion species delimitation of California endemic trapdoor spiders within theEcology and evolution · 2023Article
- High-resolution species assignment ofeLife · 2022Article
- Excluding Loci With Substitution Saturation Improves Inferences From Phylogenomic Data.Systematic biology · 2022Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats. Many fields of science are now using machine learning, and in evolutionary biology supervised machine learning has recently been used to infer species boundaries. These supervised methods require training data with associated labels. Conversely, unsupervised machine learning (UML) uses inherent data structure and does not require user-specified training labels, potentially providing more objectivity in species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three UML approaches (random forests, variational autoencoders, t-distributed stochastic neighbor embedding) for species delimitation in an arachnid taxon with high population genetic structure (Opiliones, Laniatores, Metanonychus). We find that UML approaches successfully cluster samples according to species-level divergences and not high levels of population structure, while model-based validation methods severely over-split putative species. UML offers intuitive data visualization in two-dimensional space, the ability to accommodate various data types, and has potential in many areas of systematic and evolutionary biology. We argue that machine learning methods are ideally suited for species delimitation and may perform well in many natural systems and across taxa with diverse biological characteristics.
Indexed as
Identifiers
What OpenQuestion holds
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.