Discovering and Summarizing Relationships Between Chemicals, Genes, Proteins, and Diseases in PubChem

• Published in: Frontiers in research metrics and analytics Vol. 6; p. 689059
• Main Authors: Zaslavsky, Leonid, Cheng, Tiejun, Gindulyte, Asta, He, Siqian, Kim, Sunghwan, Li, Qingliang, Thiessen, Paul, Yu, Bo, Bolton, Evan E.
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 12.07.2021
• Subjects: data mining; information retrieval; knowledge discovery; knowledge panels; knowledge summarization; natural language processing; Research Metrics and Analytics
• ISSN: 2504-0537; 2504-0537
• DOI: 10.3389/frma.2021.689059

The literature knowledge panels developed and implemented in PubChem are described. These help to uncover and summarize important relationships between chemicals, genes, proteins, and diseases by analyzing co-occurrences of terms in biomedical literature abstracts. Named entities in PubMed records are matched with chemical names in PubChem, disease names in Medical Subject Headings (MeSH), and gene/protein names in popular gene/protein information resources, and the most closely related entities are identified using statistical analysis and relevance-based sampling. Knowledge panels for the co-occurrence of chemical, disease, and gene/protein entities are included in PubChem Compound, Protein, and Gene pages, summarizing these in a compact form. Statistical methods for removing redundancy and estimating relevance scores are discussed, along with benefits and pitfalls of relying on automated (i.e., not human-curated) methods operating on data from multiple heterogeneous sources.