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

Abstract

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.

Studying the Parkinson’s disease metabolome and exposome in biological samples through different analytical and cheminformatics approaches: a pilot study

“…Furthermore, PubChem functionality relating Medical Subject Headings (MeSH) information on certain disease endpoints to chemicals in PubChem was explored to create additional lists and databases of chemicals associated with PD and related disorders [ 23 ]. Firstly, chemicals co-occurring with MeSH terms (D000544, D003704, D006816, D010300, D010302, D019636, D020734, and D020961) were merged into a single MetFrag database (PD-MetFrag).…”

“…In response to this need, open databases have emerged to assist in identification efforts, including the MassBank of North America (MoNA) [ 19 ] for mass spectral matching, as well as the Human Metabolome Database [ 20 ] for metabolomics or PubChemLite for Exposomics [ 21 ], a smaller subset of the 111-million-entry open chemistry database PubChem [ 22 ]. PubChem also added functionality to cross-link relationships between chemicals and diseases [ 23 ], which could be leveraged for exposomics.…”

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“…PubChem is a popular public repository for chemical substance information resources and their biological activities that serves the scientific community as well as the general public by the NIH. Many studies have used PubChem, including those that are used to train machine learning/deep learning algorithms, which are used to predict the drug targets, 14 in order to uncover and summarize important relationships among chemicals, genes, proteins, and diseases by analyzing co-occurrences of terms in biomedical literature abstracts, 15 and to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulatory system through text mining and database fusion. 16 …”