semvec/pypi-packages huggingface dataset:
flask-neon-kit 0.0.6
Automatically generates Flask CRUD endpoints from defined Neon Postgres models
License: MIT
Keywords: neon-oss-starter-kit, flask-neon-kit, flask-crud

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Flask-Neon-Kit
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flask-neon-kit is a Flask extension which generates CRUD endpoints out of the box from defined models of the Neon Postgres database. This solution is built upon the foundation of the Flask-SQLAlchemy. This initiative was driven by the tedious nature of manually writing CRUD logic for every Flask application entity.

Features
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- Automatically generates CRUD endpoints from defined model.
- Allows to customize app base URL as well as each model's url prefix.
- Allows to paginate when getting many entities from database table.

Installation
===============
You can install flask-neon-kit via Python Package Index:

.. code:: bash

    pip install flask-neon-kit

Documentation
===============

`See Flask Neon Kit's Documentation. <https://github.com/ValentineSean/flask-neon-kit>`_
GraphCVAE 1.0.3
GraphCVAE: Uncovering Cell Heterogeneity and Therapeutic Target Discovery through Residual and Contrastive Learning
License: MIT

GraphCVAE is a comprehensive model that integrates gene expression, spatial coordinates, and tissue morphology to accurately define spatial domains. The model first processes tissue image data to generate a morphological feature matrix, then combines spatial proximity information and gene expression similarity to enhance gene expression data. GraphCVAE employs multi-layer Graph Convolutional Networks (GCN) and a Variational Autoencoder (VAE) to optimize spatial domain representation. GCN captures complex spatial relationships, while VAE transforms high-dimensional data into a low-dimensional latent space. To improve model robustness, GraphCVAE adopts a contrastive learning approach, distinguishing between original and shuffled spatial gene expression data. The model accurately captures spatial patterns and gene expression features by combining reconstruction loss, KL divergence loss, and contrastive loss. The resulting embeddings are used for downstream analysis, including spatial domain identification, batch effect correction, and other spatial transcriptomics-related tasks, revealing tissue spatial structure and deepening understanding of gene expression patterns.