Orange3-Bioinformatics

v4.8.7 safe
3.0
Low Risk

Orange Bioinformatics add-on for Orange data mining software package.

🤖 AI Analysis

Final verdict: SAFE

The package shows some signs of obfuscation, but overall the risks are low with no evidence of malicious activities such as shell execution or credential theft.

  • Potential obfuscation observed
  • No shell execution detected
Per-check LLM notes
  • Network: The observed network patterns are likely legitimate for checking file modification times before downloading, which is common in software updates or dependency management.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The observed patterns suggest potential obfuscation but may also be part of normal package behavior for dynamic imports and data deserialization.
  • Credentials: No clear evidence of credential harvesting or secret handling was found.
  • Metadata: The maintainer has only one package, suggesting it might be a new or less active account, but no other red flags are present.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • al_filename): r = requests.get(url, stream=True) modified_since = r.headers['la
  • t(url, None) r = requests.get(url, headers=dict([("If-Modified-Since", modified_since)]),
  • = {} self._session = requests.Session() self._session.headers.update( {'Accept
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • " + ontology[term_id].name) __import__("pkg_resources").declare_namespace(__name__) # orangecontrib is a namespace
  • try: return pickle.loads(pickle_str) except Exception: ra
✓ 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: biolab.si

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository biolab/orange3-bioinformatics appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Bioinformatics Laboratory, FRI UL" 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 Orange3-Bioinformatics 
Create a bioinformatics mini-app using the 'Orange3-Bioinformatics' package in Python. Your app should allow users to analyze gene expression data from microarray experiments. Here’s a detailed breakdown of the project requirements:

1. **Project Title**: MicroArray Expression Analyzer (MAEA)
2. **Core Functionality**:
   - Load gene expression data from microarray files (e.g., .CEL files).
   - Preprocess the data to normalize and correct for background noise.
   - Perform differential expression analysis to identify genes that are significantly upregulated or downregulated between different conditions (e.g., treated vs untreated samples).
   - Visualize the results through heatmaps and volcano plots.
3. **Features**:
   - User-friendly GUI built with Orange's widgets.
   - Ability to import multiple datasets and compare them side-by-side.
   - Option to adjust parameters for differential expression analysis (e.g., p-value threshold, fold change).
   - Exporting of analysis results into common formats like CSV or Excel.
4. **Utilization of 'Orange3-Bioinformatics' Package**:
   - Use the package's widgets for loading and preprocessing microarray data.
   - Leverage the package's functionality for normalization techniques such as Robust Multi-array Average (RMA).
   - Employ the package's tools for performing statistical tests on gene expression levels.
   - Utilize the package's visualization capabilities to generate high-quality plots.
5. **Development Steps**:
   - Set up a Python environment with the necessary packages installed, including 'Orange3-Bioinformatics'.
   - Design the user interface using Orange's widget framework.
   - Implement the data loading, preprocessing, and analysis functionalities.
   - Integrate the visualization components.
   - Test the app with real microarray datasets to ensure accuracy and usability.
6. **Expected Outcome**:
   - A fully functional desktop application that simplifies the process of analyzing microarray data for researchers without requiring extensive programming knowledge.