ArticleNPJ systems biology and applications2023
A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape.
Article in NPJ systems biology and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Deep learning reveals ecological decoupling and rate heterogeneity in Hymenoptera wing evolution.Science advances · 2026Article
- Phylogenetic Inference from Atomized 3D Morphometric Data: A Case Study using Kangaroos.Systematic biology · 2026Article
- Artificial-Intelligence-Based Cephalometric Landmark Detection in Lateral Cephalograms.Journal of clinical medicine · 2026Article
- Artificial intelligence for design strategies of tissue engineering materials.Fundamental research · 2026Review
- Connecting links with code: AI in morphological evolution.Frontiers in bioinformatics · 2026Review
- Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Visualizing the multidimensional landscape of biological variation in modern microscopy.Frontiers in bioinformatics · 2026Article
- Machine learning for experimental design of ultrafast electron diffraction.Scientific reports · 2025Article
- Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics: Lessons from Digitized Collections.Systematic biology · 2025Article
- Representation of high-dimensional cell morphology and morphodynamics in 2D latent space.Physical biology · 2025Article
- A Novel Approach to Study Cranial Shape in Children with Normal and Sagittal Synostosis: An Application of Geometric Morphometrics.Neurologia medico-chirurgica · 2025Article
- DUNE: a versatile neuroimaging encoder captures brain complexity across 3 major diseases: cancer, dementia, and schizophrenia.GigaScience · 2025Article
- A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data.Computational and structural biotechnology journal · 2025Article
- Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations.Communications physics · 2025Article
- Teeth Microcracks Research: Towards Multi-Modal Imaging.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Shape measurements are crucial for evolutionary and developmental biology; however, they present difficulties in the objective and automatic quantification of arbitrary shapes. Conventional approaches are based on anatomically prominent landmarks, which require manual annotations by experts. Here, we develop a machine-learning approach by presenting morphological regulated variational AutoEncoder (Morpho-VAE), an image-based deep learning framework, to conduct landmark-free shape analysis. The proposed architecture combines the unsupervised and supervised learning models to reduce dimensionality by focusing on morphological features that distinguish data with different labels. We applied the method to primate mandible image data. The extracted morphological features reflected the characteristics of the families to which the organisms belonged, despite the absence of correlation between the extracted morphological features and phylogenetic distance. Furthermore, we demonstrated the reconstruction of missing segments from incomplete images. The proposed method provides a flexible and promising tool for analyzing a wide variety of image data of biological shapes even those with missing segments.
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.