Abstract: This disclosure enables various technologies that can (1) learn new synonyms for a given concept without manual curation techniques, (2) relate (e.g., map) some, many, most, or all raw named entity recognition outputs (e.g., “United States”, “United States of America”) to ontological concepts (e.g., ISO-3166 country code: “USA”), (3) account for false positives from a prior named entity recognition process, or (4) aggregate some, many, most, or all named entity recognition results from machine learning or rules based approaches to provide a best of breed hybrid approach (e.g., synergistic effect).

Abstract: This disclosure enables various technologies that can (1) learn new synonyms for a given concept without manual curation techniques, (2) relate (e.g., map) some, many, most, or all raw named entity recognition outputs (e.g., “United States”, “United States of America”) to ontological concepts (e.g., ISO-3166 country code: “USA”), (3) account for false positives from a prior named entity recognition process, or (4) aggregate some, many, most, or all named entity recognition results from machine learning or rules based approaches to provide a best of breed hybrid approach (e.g., synergistic effect).

Abstract: A system of generating a suitability and/or toxicity score for a compound of interest is described. The system may be trained by obtaining physical properties of compounds; retrieving or predicting physiological binding sites or targets of the compound; and using clinical data for known compounds and interactions. Suitability or toxicity for a compound of interest may be obtained based upon a machine-learning prediction. In such a system, the clinical descriptions for the compound may include at least one of an indication for use of the compound and a therapeutic area of the compound, and also may include the use of gene expression strength metrics.