ArticleScience advances2019
Deep learning on butterfly phenotypes tests evolution's oldest mathematical model.
Article in Science advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Deep learning reveals ecological decoupling and rate heterogeneity in Hymenoptera wing evolution.Science advances · 2026Article
- Beyond the Classics: The Synergy of AI and Genomics Reveals an Expanded Repertoire of Pigmentation Genes.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2026Review
- Connecting links with code: AI in morphological evolution.Frontiers in bioinformatics · 2026Review
- Integrative analyses reveal the evolution of the Old World Swallowtail in the Palearctic.PloS one · 2026Article
- Deep learning reveals the complex genetic architecture of male guppy colouration.Nature ecology & evolution · 2025Article
- Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics: Lessons from Digitized Collections.Systematic biology · 2025Article
- Diel activity correlates with colour pattern morphology of heterobranch sea slugs.The Journal of animal ecology · 2025Article
- Measuring homoplasy I: comprehensive measures of maximum and minimum cost under parsimony across discrete cost matrix character types.Cladistics : the international journal of the Willi Hennig Society · 2025Article
- A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data.Computational and structural biotechnology journal · 2025Article
- InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline.PNAS nexus · 2025Article
- Dorsoventral comparison of intraspecific variation in the butterfly wing pattern using a convolutional neural network.Biology letters · 2025Article
- Male and female contributions to diversity among birdwing butterfly images.Communications biology · 2024Article
- Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes.PNAS nexus · 2024Article
- Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis.Evolutionary ecology · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Convergence in sympatric swallowtail butterflies reveals ecological interactions as a key driver of worldwide trait diversification.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape.NPJ systems biology and applications · 2023Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Morphology-Based Identification of Bemisia tabaci Cryptic Species Puparia via Embedded Group-Contrast Convolution Neural Network Analysis.Systematic biology · 2022Article
- What you sample is what you get: ecomorphological variation in Trithemis (Odonata, Libellulidae) dragonfly wings reconsidered.BMC ecology and evolution · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of
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Registered trials
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