adamixture

v1.6.1 safe
3.0
Low Risk

ADAMIXTURE: Adaptive First-Order Optimization for Biobank-Scale Ancestry Inference

🤖 AI Analysis

Final verdict: SAFE

The package is assessed as safe with low risk indicators. It does not perform network calls or shell executions, which are common vectors for malicious activities.

  • No network calls detected.
  • No shell execution patterns detected.
  • Metadata risk due to new or inactive maintainer account.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate low activity or lack of commitment.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AI-sandbox/ADAMIXTURE 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 adamixture 
Create a mini-application named 'AncestryAnalyzer' using the Python package 'adamixture'. This application will help users infer their genetic ancestry based on a set of SNP (Single Nucleotide Polymorphism) data. The app should include a user-friendly interface where users can upload their SNP data in a common format like VCF (Variant Call Format). Upon submission, the app should process the data using 'adamixture', which employs adaptive first-order optimization techniques specifically tailored for large-scale biobank datasets. The output should provide a breakdown of the user's genetic ancestry proportions across different populations.

Key Features:
1. User Interface: Develop a simple web-based UI allowing users to upload SNP data files.
2. Data Validation: Implement checks to ensure the uploaded file is in the correct format and contains valid SNP data.
3. Processing Engine: Use 'adamixture' to analyze the SNP data and infer ancestry information.
4. Results Presentation: Display the ancestry breakdown in a visually appealing way, such as a pie chart or bar graph.
5. Documentation: Provide comprehensive documentation explaining how to use the application, including any necessary setup steps and examples of expected input/output.

The goal is to create a tool that not only showcases the capabilities of 'adamixture' but also makes complex genetic analysis accessible to a broader audience.