ArticlePNAS nexus2024
Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Balanced DNA interpolation improves learning of genetic distance-informed embeddings in plants.PLoS computational biology · 2026Article
- Climate adaptation and functional constraints drive pollen evolution in Apiales.The New phytologist · 2026Article
- Article
- Connecting links with code: AI in morphological evolution.Frontiers in bioinformatics · 2026Review
- Digitizing microscope slide-based natural history collections: A protocol using slide scanner technology.PloS one · 2026Article
- Pollen morphology, deep learning, phylogenetics, and the evolution of environmental adaptations in Podocarpus.The New phytologist · 2025Article
- The evolutionary history evident in grass pollen morphology.The New phytologist · 2025Article
- A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data.Computational and structural biotechnology journal · 2025Article
- Pollen image manipulation and projection using latent space.Frontiers in plant science · 2025Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The phylogenetic interpretation of pollen morphology is limited by our inability to recognize the evolutionary history embedded in pollen features. Deep learning offers tools for connecting morphology to phylogeny. Using neural networks, we developed an explicitly phylogenetic toolkit for analyzing the overall shape, internal structure, and texture of a pollen grain. Our analysis pipeline determines whether testing specimens are from known species based on uncertainty estimates. Features from specimens with uncertain taxonomy are passed to a multilayer perceptron network trained to transform these features into predicted phylogenetic distances from known taxa. We used these predicted distances to place specimens in a phylogeny using Bayesian inference. We trained and evaluated our models using optical superresolution micrographs of 30 extant
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.