autoencodix

v0.2.2 suspicious
4.0
Medium Risk

Framework for multi-omics data integration by autoencoders.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package autoencodix v0.2.2 shows signs of potential obfuscation aimed at concealing its functionality, coupled with metadata that suggests a less established author. While there are no direct indicators of malicious activity, the combination of these factors raises some concerns.

  • Unusual obfuscation patterns
  • Inadequate author metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function.
  • Shell: No shell execution detected, indicating the package does not execute system commands.
  • Obfuscation: The observed obfuscation pattern seems to be related to model evaluation within a machine learning context, but the unusual method of obfuscation may indicate an attempt to hide code functionality.
  • Credentials: No suspicious patterns for credential harvesting were detected.
  • Metadata: The author's name is missing or very short and the author has only one package on PyPI, which may indicate a less established or potentially suspicious account.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (5500 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 259 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 10 unique contributor(s) across 100 commits in jan-forest/autoencodix_package
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • self._trainer._model.eval() with torch.no_grad(), self._trainer._fabric.autoca
✓ 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: uni-leipzig.de>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository jan-forest/autoencodix_package 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 autoencodix 
Create a mini-application called 'OmicsExplorer' using the Python package 'autoencodix'. This application will serve as a tool for researchers to integrate multiple types of omics data (such as genomics, proteomics, metabolomics) into a unified model using autoencoders. The goal is to provide a user-friendly interface where users can upload their omics datasets and receive integrated insights and visualizations.

### Steps:
1. **Setup**: Install the necessary packages including 'autoencodix', 'pandas', 'numpy', 'matplotlib', and any other dependencies required for data handling and visualization.
2. **Data Input Interface**: Develop a simple command-line interface or a web-based form where users can input URLs or local file paths to upload their omics datasets. Ensure that the application supports common file formats such as CSV, TSV, or Excel.
3. **Data Preprocessing**: Implement functionality within 'autoencodix' to preprocess the uploaded datasets. This includes normalization, imputation of missing values, and feature scaling if necessary.
4. **Model Training**: Use 'autoencodix' to train an autoencoder model on the preprocessed data. Users should be able to specify parameters such as the number of layers, activation functions, and learning rate.
5. **Integration Analysis**: After training, use the autoencoder to perform dimensionality reduction and generate an integrated representation of the omics data. This step involves applying the trained model to reduce the complexity of the data while retaining important biological signals.
6. **Visualization**: Provide visual representations of the integrated data through plots such as scatter plots, heatmaps, or PCA plots. Allow users to explore these visualizations interactively.
7. **Output**: Enable users to download the integrated dataset and visualization results in various formats such as CSV, PNG, or PDF.
8. **Documentation**: Write comprehensive documentation explaining each step of the process, the rationale behind using autoencoders for omics data integration, and how to interpret the results.

### Suggested Features:
- **Parameter Tuning Interface**: Allow users to adjust hyperparameters like batch size, epochs, and layer sizes.
- **Multi-omics Dataset Support**: Ensure compatibility with various types of omics data.
- **Batch Processing**: Implement batch processing capabilities for handling large datasets efficiently.
- **Interactive Visualizations**: Integrate interactive elements into the visualizations to allow users to explore different aspects of the data dynamically.

💬 Discussion Feed

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