scientific-text-mining-nlp

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.

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.

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.

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.

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.

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.