Summarizing relationships between chemicals, genes, proteins, and diseases in PubChem using analysis of their co-occurrences in patents

The knowledge panels in PubChem allow users to quickly identify and summarize important relationships between chemicals, genes, proteins, and diseases by analyzing the co-occurrences of those entities in a collection of text documents. In the present study, the analysis and summarization techniques used to develop the literature knowledge panels in PubChem were extended to patent documents from the Google Patent Research Data (GPRD) set. The annotations of the patent documents in the GPRD set were mapped to NCBI database records to create the patent co-occurrence data. The annotations were not only from the titles and abstracts of patents but also from other parts such as claims and descriptions, greatly improving the coverage of the co-occurrence-based entity relationships in PubChem. Informativeness weights of entities were introduced in the co-occurrence and relevance score computations to account for a significant variation in the number of matched annotations per patent section. This narrows the focus to the co-occurrences that are more relevant to the subject matter of the patent. The resulting co-occurrence data was used to generate the patent knowledge panels, enabling users to identify entities co-mentioned in patents alongside a specific chemical or gene. The patent co-occurrence data can be downloaded interactively or accessed programmatically. Overall, the patent knowledge panels described in this study provide users with quick access to essential biomedical entities associated with a given PubChem record. Users can delve into relevant patent documents related to these entities or download the underlying co-occurrence data for further exploration and analysis.