anukriti-pgx-core

v0.4.0 suspicious
6.0
Medium Risk

Deterministic pharmacogenomics infrastructure: CPIC-pinned phenotype engine, gene callers, and recommendation lookup.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity such as network calls or shell executions. However, the metadata risk score is elevated due to the untraceable repository and the maintainer's single package, raising concerns about potential supply-chain risks.

  • Elevated metadata risk score due to untraceable repository.
  • Maintainer has only one package listed.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No secret harvesting patterns detected, indicating safe handling of credentials or lack thereof.
  • Metadata: The repository is not found, and the maintainer has a single package which raises suspicion but does not conclusively indicate malice.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. test_anukriti_parity.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/AnukritiAi-hq/anukriti-pgx-core#readme
  • Detailed PyPI description (5591 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

  • Classifier: Typing :: Typed
  • 52 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Anukriti contributors" 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 anukriti-pgx-core 
Your task is to develop a comprehensive mini-application that integrates the 'anukriti-pgx-core' package to create a user-friendly interface for pharmacogenomic analysis. This application will serve as a tool for healthcare professionals to assess patient-specific drug responses based on genetic information. Here’s a detailed breakdown of what your application should achieve:

1. **User Interface**: Design a simple yet intuitive web-based UI using Flask or Django. This UI should allow users to input patient genetic data in a standard format such as VCF files.
2. **Data Processing**: Utilize the 'anukriti-pgx-core' package to process the uploaded genetic data. Specifically, use its gene caller functionality to identify relevant genetic variants associated with drug metabolism.
3. **Phenotype Engine**: Apply the CPIC-pinned phenotype engine provided by 'anukriti-pgx-core' to predict potential drug response phenotypes based on identified genetic variants.
4. **Recommendation Lookup**: Use the recommendation lookup feature to provide personalized drug recommendations along with evidence supporting these recommendations.
5. **Report Generation**: Develop a feature that generates a detailed report summarizing the patient's genetic profile, predicted drug responses, and recommended treatments. This report should be downloadable in PDF format.
6. **Security Measures**: Ensure that all patient data is handled securely, including encryption of sensitive information and compliance with HIPAA regulations.
7. **Testing and Validation**: Implement rigorous testing procedures to validate the accuracy of the genetic data processing and recommendation generation. Include unit tests for backend functionalities and integration tests for the entire system.

**Suggested Features**:
- Integration with common laboratory information systems (LIS) for seamless data import.
- A dashboard that provides visual summaries of drug response predictions.
- Real-time alerts for critical drug-gene interactions.
- Option to export data in various formats for further analysis.

Your application should demonstrate a deep understanding of pharmacogenomics and how it impacts personalized medicine. Make sure to document each component thoroughly and provide clear instructions for installation and usage.

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