🤖 AI Analysis

Final verdict: SAFE

The package GenKI v0.2.1 has been assessed and found to have low risk indicators with no network or shell risks detected. The package appears safe for use based on the provided information.

No network calls detected
No shell execution detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction.
Shell: No shell execution detected, indicating no direct system command execution by the package.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

): self.model.eval() z = self.model.encode(self.test_data.x, se
""" self.model.eval() _ = self.model.encode(data.x, data.edge_index)
.no_grad(): model.eval() z = model.encode(val_data.x, val_data.edge_ind
): best_trained_model.eval() z = best_trained_model.encode(test_data.x, test_da

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: tamu.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository yjgeno/GenKI appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with GenKI

Create a mini-application that leverages the GenKI package to infer gene knock-out effects from single-cell RNA sequencing data using variational graph autoencoders. Your application should enable users to upload their single-cell RNA sequencing datasets, preprocess the data, apply the GenKI model for inference, and visualize the results. Here’s a detailed breakdown of the steps and features your application should include:

1. **Data Upload**: Allow users to upload their single-cell RNA sequencing datasets in standard formats like `.h5ad` (AnnData) or `.loom`. Ensure the application checks the validity of the uploaded file and provides feedback if the format is incorrect.
2. **Preprocessing**: Implement basic preprocessing steps such as normalization, log-transformation, and batch correction (if applicable). Provide options for users to select specific preprocessing methods based on their dataset characteristics.
3. **Model Application**: Use the GenKI package to apply variational graph autoencoders to the preprocessed data. The application should allow users to specify parameters for the inference process, such as the number of epochs, learning rate, and other hyperparameters relevant to the GenKI model.
4. **Inference Visualization**: After running the inference, display the results in an interactive format, such as heatmaps showing gene expression changes post-knockout, or graphs visualizing the relationships between genes. Enable users to explore these visualizations in detail.
5. **Result Export**: Provide functionality for users to export their results in various formats, including CSV for tabular data and PNG/JPEG for images, so they can further analyze or present their findings.
6. **Documentation & Help**: Include comprehensive documentation within the application to guide users through each step of the process, explaining the significance of different parameters and how to interpret the results. Also, provide a FAQ section addressing common issues users might face.

Your goal is to create a user-friendly interface that makes it easy for researchers and bioinformaticians to leverage the power of GenKI without needing extensive knowledge about the underlying computational methods.