Verified:GitHub

scientific-text-mining-nlp

Extract biomedical entities and relationships from scientific text into a literature knowledge graph.

3|1|Updated Feb 11, 2026

Search Tags:#biomedical #knowledge-graph #topic-modeling #citation-network #ner #relation-extraction #evidence-summarization

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-text-mining-nlp

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-text-mining-nlp
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-text-mining-nlp
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-text-mining-nlp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automatically extract biomedical entities and relationships from scientific text to enable scalable literature analysis and knowledge discovery.

Core Features & Use Cases

Entity recognition: biomedical NER for genes/proteins, diseases, drugs, and chemicals.
Relation extraction & knowledge graph: build literature-based graphs capturing interactions (PPI, DDI, GDA) and pathways.
Topic modeling & evidence summarization: identify research topics and provide concise evidence summaries.
Citation network analysis: analyze influence and connections among cited papers.

Quick Start

Run an end-to-end biomedical NLP pipeline to extract entities, relations, and build a literature knowledge graph from a given corpus.

Frequently Asked Questions about scientific-text-mining-nlp

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

FAQPage Schema

How do I extract biomedical entities and relationships from scientific text for literature reviews?▼

You can extract biomedical entities and relationships from scientific text using NER and relation extraction pipelines. This process builds a literature knowledge graph capturing interactions like PPI, DDI, and GDA from PubMed, EuropePMC, and Semantic Scholar sources.

What is a literature knowledge graph and how does it support drug discovery?▼

A literature knowledge graph is a structured representation of biomedical entities and their interactions extracted from scientific text. It supports drug discovery by mapping relationships between genes, proteins, diseases, and chemicals across large research corpora.

Can I use PubMed and Semantic Scholar data for topic modeling and evidence summarization?▼

Yes, you can process PubMed and Semantic Scholar data for topic modeling and evidence summarization. The pipeline identifies research topics and generates concise evidence summaries from scientific literature.

How do I build a citation network analysis to analyze research paper influence?▼

Citation network analysis is built by extracting and mapping connections among cited papers from scientific literature sources. This analyzes influence and relationships, helping you understand research trends and key publications.

Does biomedical NLP support named entity recognition for genes, diseases, and chemicals?▼

Biomedical NLP supports named entity recognition for genes, proteins, diseases, drugs, and chemicals. This entity extraction is integrated with PubTator and Semantic Scholar data to enable scalable literature analysis.

What's the best way to construct a literature knowledge graph from biomedical research papers?▼

The best way to construct a literature knowledge graph is running an end-to-end biomedical NLP pipeline. This performs NER, relation extraction, and graph construction to capture pathways and interactions across your research corpus.