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MubasherMohammed
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claw-semantic-sim

Compute semantic connectivity metrics for disease research using PubMedBERT embeddings.

Updated May 10, 2026
Search Tags:#bioinformatics#semantic analysis#biomedical research#disease neglect#research metrics
One-click install
npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill claw-semantic-sim-mubashermohammed
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: claw-semantic-sim
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/claw-semantic-sim
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill claw-semantic-sim-mubashermohammed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, transformers, h5py, umap-learn, biopython, networkx, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps researchers assess the connectivity and neglect of disease research within the biomedical literature using semantic similarity metrics.

Core Features & Use Cases

  • Semantic Isolation Index (SII): Measures the distance of disease research to its nearest neighbors to identify isolated research areas.
  • Knowledge Transfer Potential (KTP): Evaluates the similarity between disease research centroids for potential cross-disciplinary research.
  • Research Clustering Coefficient (RCC): Assesses the variance in research approaches within a disease.
  • Temporal Semantic Drift: Tracks changes in research focus over time.
  • Visualization: Generates multi-panel figures for comprehensive analysis.
  • Reporting: Provides markdown reports with metrics, rankings, and reproducibility.

Quick Start

Run the semantic_sim.py script with a list of diseases to compute semantic similarity metrics.

Frequently Asked Questions about claw-semantic-sim

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I measure semantic connectivity of disease research in biomedical literature?▼

Semantic connectivity of biomedical literature is measured using PubMedBERT embeddings and computational metrics to calculate the semantic distance between disease research areas. This identifies isolated research topics and potential knowledge spillover opportunities.

What is the Semantic Isolation Index for identifying neglected disease research?▼

The Semantic Isolation Index (SII) measures the distance of disease research to its nearest neighbors within the biomedical literature. It identifies isolated or neglected research areas by calculating semantic similarity metrics using PubMedBERT embeddings.

How do I assess knowledge transfer potential between different disease research areas?▼

Knowledge Transfer Potential (KTP) evaluates the similarity between disease research centroids to identify cross-disciplinary research opportunities. It is calculated alongside the Research Clustering Coefficient to assess variance in research approaches within a disease.

Can I track temporal semantic drift in biomedical research publications over time?▼

Temporal semantic drift tracks changes in research focus over time by analyzing PubMedBERT embeddings of biomedical literature. It generates multi-panel visualizations and markdown reports with metrics, rankings, and reproducibility information.

Do I need PyTorch and transformers to compute biomedical semantic similarity metrics?▼

Computing biomedical semantic similarity metrics requires PyTorch, transformers, and additional dependencies including h5py, umap-learn, biopython, and networkx. These libraries support PubMedBERT embeddings, quality filtering, and statistical analysis.

What's the best way to visualize research neglect and clustering in biomedical literature?▼

Visualizing research neglect and clustering is achieved by generating multi-panel figures from computed semantic metrics like the Semantic Isolation Index and Research Clustering Coefficient. The analysis produces comprehensive markdown reports with rankings and reproducibility details.

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