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cancer-researcher

Map overlapping Ayurvedic phytochemical and cancer drug targets across processed CSV datasets.

4|1|Updated Jan 8, 2024
Search Tags:#knowledge graph#drug resistance#precision medicine#oncology analysis#biomarker mapping#drug target overlap#Ayurvedic compounds
One-click install
npx skills add https://github.com/OpenSourcePharmaFoundation/ospf-ayurveda-kg --skill cancer-researcher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cancer-researcher
Source: https://github.com/OpenSourcePharmaFoundation/ospf-ayurveda-kg/tree/main/.claude/skills/cancer-researcher
Command: npx skills add https://github.com/OpenSourcePharmaFoundation/ospf-ayurveda-kg --skill cancer-researcher

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you connect Ayurvedic phytochemicals in the OSPF knowledge graph to modern oncology hypotheses by mapping overlapping molecular targets, pathways, biomarkers, and resistance contexts.

Core Features & Use Cases

  • Target overlap & mechanism mapping: Identifies shared proteins between phytochemical target interactions and known cancer drug targets/mechanisms.
  • Pathway and indication relevance: Evaluates whether shared targets sit in established cancer signaling pathways and checks indication links in drug-indication data.
  • Precision and resistance-aware reasoning: Frames findings in clinical trial/biomarker/combination-resistance terms with explicit confidence levels and data-vs-inference separation for hypothesis generation.

Quick Start

Use the cancer-researcher skill to analyze which phytochemical targets in the repository are most likely to overlap with oncology drug targets and to summarize biomarker and resistance-relevant implications for repurposing hypotheses.

Frequently Asked Questions about cancer-researcher

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

FAQPage Schema
How do I map Ayurvedic phytochemicals to cancer drug targets for drug repurposing?▼

Target overlap analysis connects Ayurvedic phytochemical targets to oncology drug mechanisms by finding shared protein interactions. It evaluates whether these overlapping targets sit in established cancer signaling pathways and checks their indication links using drug-indication datasets.

Can I generate drug resistance combination hypotheses using a knowledge graph of Ayurvedic compounds?▼

Yes, you can generate resistance-aware combination hypotheses by mapping overlapping phytochemical and cancer drug targets within the knowledge graph. This frames findings in clinical trial, biomarker, and resistance contexts to support precision medicine oncology analysis.

Does this oncology analysis approach require processed CSV datasets for biomarker mapping?▼

Yes, this oncology analysis requires processed CSV datasets from project data sources to ground biomarker mapping and citations. It relies on these structured files to find overlaps between phytochemical targets and known cancer drug targets.

How are inferred statements separated from known data when analyzing drug target overlap?▼

Analyzing drug target overlap requires strict separation between known data and inferred statements. The process mandates confidence-level labeling and CSV-file grounded citations to distinguish established oncology mechanisms from generated phytochemical hypotheses.

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