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Generative modeling of biological shapes and images using a probabilistic -shape sampler.
Emily T Winn-Nuñez1, Hadley Witt2,3, Dhananjay Bhaskar4
1Division of Applied Mathematics, Brown University, Providence, RI, USA.
Biorxiv : the Preprint Server for Biology
|January 23, 2024
Summary
We introduce the A-shape sampler, a new probabilistic framework for generating realistic 2D and 3D shapes. This tool aids in understanding morphological variation in computational biology, especially when real data is scarce.
Area of Science:
- Computational biology
- Biostatistics
- Geometric morphometrics
Background:
- Understanding morphological variation is crucial in computational biology.
- Current methods for shape analysis often struggle with limited or inaccessible real-world data.
- Existing generative models for shapes lack systematic assessment capabilities.
Conclusions:
- The A-shape sampler offers a flexible and powerful approach for modeling morphological variation.
- This framework addresses limitations in data availability and generative model assessment in shape analysis.
- The open-source availability promotes wider adoption and further development in computational biology.
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